i IMPLEMENTATION OF LUO-RUDY PHASE I CARDIAC...
58
IMPLEMENTATION OF LUO-RUDY PHASE I CARDIAC CELL EXCITATION MODEL IN FPGA NORLIZA BINTI OTHMAN A thesis submitted in fulfillment of the requirement for the award of the Degree of Master of Electrical Engineering Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia APRIL 2017
Transcript of i IMPLEMENTATION OF LUO-RUDY PHASE I CARDIAC...
i
IMPLEMENTATION OF LUO-RUDY PHASE I CARDIAC CELL EXCITATION
MODEL IN FPGA
NORLIZA BINTI OTHMAN
A thesis submitted in
fulfillment of the requirement for the award of the
Degree of Master of Electrical Engineering
Faculty of Electrical and Electronic Engineering
Universiti Tun Hussein Onn Malaysia
APRIL 2017
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
iii
For my beloved family and
to everyone who supports me, it just begins…
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
iv
ACKNOWLEDGEMENT
First of all, I would like to thank the Almighty ALLAH for His power and His
blessing to me to complete my Master research study.
Special thanks to my Supervisor, Dr. Farhanahani binti Mahmud and my Co-
supervisor, Assoc. Prof. Dr. Abdul Kadir Mahamad for guiding and supporting me
over this journey. I would like to thank lecturers from computer engineering
department, Dr. Mohamad Hairol bin Jabbar and Assoc. Prof. Dr. Afandi bin Ahmad
that always guide me to complete the research.
I also would like to thank the Fundamental Research Grant Scheme (FRGS)
of Vot. Number 1053, Ministry of Higher Education Malaysia. Besides, I also would
like to thank the Research, Innovation, Commercialization, Consultancy Office
(ORICC), UTHM for the Postgraduate Incentive Research Grant (GIPS), Vot.
Number 1371.
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
v
ABSTRACT
Dynamic simulation of complex cardiac excitation and conduction requires high
computational time. Thus, the hardware techniques that can run in the real-time
simulation was introduced. However, previously developed hardware simulation
requires high power consumption and has a large physical size. Due to the
drawbacks, this research presents the adaptation of Luo-Rudy Phase I (LR-I) cardiac
excitation model in a rapid prototyping method of field programmable gate array
(FPGA) for real-time simulation, lower power consumption and minimizing the
size. For the rapid prototyping, a nonlinear Ordinary Differential Equation (ODE)-
based algorithm of the LR-I model is implemented by using Hardware Description
Language (HDL) Coder that is capable to convert MATLAB Simulink blocks
designed into a synthesisable VHSIC Hardware Description Language (VHDL)
code and verified using the FPGA-In-the Loop (FIL) Co-simulator. The Xilinx
FPGA Virtex-6 XC6VLX240T ML605 evaluation board is chosen as a platform for
the FPGA high performance system which is supported by the HDL Coder. A fixed-
point optimisation has been successfully obtained with Percentage Error (PE) and
Mean Square Error (MSE) which are -1.08% and 2.28%, respectively. This result
has given better performance for the hardware implementation in terms of 27.5%
decrement in power consumption and 5.35% decrement in utilization area with
maximum frequency 9.819 MHz. By implementing the constructed algorithm into
the high performance FPGA system, a new real-time simulation-based analysis
technique of cardiac electrical excitation has been successfully developed.
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
vi
ABSTRAK
Simulasi dinamik pengujaan dan pengaliran jantung yang kompleks memerlukan
masa pengiraan yang tinggi. Oleh itu, teknik-teknik perkakasan yang boleh
dijalankan dalam simulasi masa nyata telah diperkenalkan. Walau bagaimanapun,
simulasi perkakasan yang dibangunkan sebelum ini memerlukan penggunaan kuasa
yang tinggi dan mempunyai saiz fizikal yang besar. Oleh kerana kelemahan
tersebut, penyelidikan ini mempersembahkan penyesuaian model pengujaan jantung
Luo-Rudy Fasa I (LR-I) dalam kaedah prototaip pantas bagi tatasusunan get boleh
atur cara medan (Field Programmable Gate Array: FPGA) untuk simulasi masa
nyata, penggunaan kuasa yang lebih rendah dan pengurangan saiz. Untuk prototaip
pantas, model LR-I berasaskan algoritma persamaan pembezaan biasa tidak linear
dilaksanakan dengan menggunakan Hardwre Description Language (HDL) Coder
yang mampu untuk menukar blok MATLAB Simulink yang direka ke dalam kod
VHSIC Hardware Description Language (VHDL) dan disahkan menggunakan
FPGA-In-Loop (FIL) Co-simulator. Papan Penilaian Xilinx FPGA Virtex-6
XC6VLX240T ML605 dipilih sebagai platform untuk sistem FPGA berprestasi
tinggi yang disokong oleh HDL Coder. Pengoptimuman titik tetap telah berjaya
diperolehi dengan Ralat Peratusan (RP) dan Ralat Min Kuasa Dua (RMKD) yang
masing-masing -1.08% dan 2.28%. Keputusan ini telah memberikan prestasi yang
lebih baik untuk pelaksanaan perkakasan dari segi 27.5% susutan dalam
penggunaan kuasa dan 5.35% susutan dalam kawasan penggunaan dengan frekuensi
maksimum 9.819 MHz. Dengan melaksanakan algoritma yang dibina ke dalam
sistem FPGA berprestasi tinggi, teknik analisis baru pengujaan elektrik jantung
berasaskan simulasi masa nyata telah berjaya dibangunkan.
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
vii
CONTENTS
TITLE i
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENT iv
ABSTRACT v
ABSTRAK vi
CONTENTS vii
LIST OF PUBLICATIONS xi
LIST OF TABLES xiii
LIST OF FIGURES xv
LIST OF SYMBOLS AND
ABBREVIATIONS xx
LIST OF APPENDICES xxiv
CHAPTER 1 INTRODUCTION 1
1.1 Overview 1
1.2 Research background 2
1.3 Problem statement 5
1.4 Research objectives 6
1.5 Research scope 6
1.6 Overall contributions 7
1.7 Thesis organisation 7
CHAPTER 2 LITERATURE REVIEW 9
2.1 Overview 9
2.2 Electrical system of the heart 11
2.3 Mechanism of Cardiac Arrhythmia 13
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
viii
2.4 Techniques of cardiac electrophysiology analysis 14
2.4.1 Experimental technique 15
2.4.1.1 In-vitro 15
2.4.1.2 In-vivo 17
2.4.2 Simulation technique 18
2.4.3.1 Computer 19
2.4.3.2 Hardware 20
2.4.3 Comparison between experimental, clinical and
model simulations techniques 22
2.5 Cardiac mathematical modeling 23
2.5.1 Ventricular cardiac mathematical modeling 24
2.5.2 Luo-Rudy Phase I model 25
2.6 Cardiac electrophysiology at the cellular level: Model
studies 27
2.6.1 Phase-locked 27
2.6.2 Voltage clamp 28
2.7 Field Programmable Gate Array (FPGA) platforms 29
2.7.1 Xilinx FPGA board 32
2.7.2 Virtex-6 Xilinx FPGA board 34
2.7.3 FPGA programming methods 37
2.7.3.1 Traditional method 38
2.7.3.2 Rapid prototyping method 41
2.7.3.3 Comparison of traditional and rapid
prototyping method 45
2.7.4 FPGA as an ODE solver 47
2.8 Hardware implementation techniques for high
performance applications 48
2.8.1 Application Specific Integrated Circuit (ASIC) 49
2.8.2 Graphical Processing Unit (GPU) 51
2.8.3 Digital Signal Processing (DSP) 51
2.8.3.1 Digital Signal Peripheral
Interface Controller (dsPIC) 52
2.8.4 Field Programmable Analog Array (FPAA) 52
2.8.5 Field Programmable Gate Array (FPGA) 53
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
ix
2.9 Research timeline of cardiac electrophysiology analysis 53
2.10 Limitation of previous research and research opportunities 57
2.11 Summary 58
CHAPTER 3 RESEARCH METHODOLOGY 59
3.1 Overview 59
3.2 Research design work flow 60
3.3 Luo-Rudy Phase I formulae 60
3.4 Solving Ordinary Differential Equations (ODEs) 70
3.5 Development of Luo-Rudy Phase I (LR-I) cardiac
model simulation based analysis system using FPGA 71
3.5.1 FPGA design using HDL Coder rapid prototyping
method 73
3.5.1.1 Floating-point to Fixed-point MATLAB
Simulink blocks design 75
3.5.1.2 System design optimisation 81
3.5.1.3 Very High Speed Integrated Circuit (VHSIC)
Hardware Description Language (VHDL)
code generation 84
3.5.1.4 FPGA-in-the-Loop (FIL) co-simulation 85
3.5.2 FPGA programming : Implementation on
Xilinx FPGA Virtex-6 evaluation board 87
3.5.2.1 Xilinx Integrated Software Environments (ISE) 88
3.5.2.2 ISE Simulator (ISim) 89
3.5.2.3 FPGA-on-board simulation: Chipscope Pro 90
3.6 Summary 94
CHAPTER 4 RESULT AND ANALYSIS 95
4.1 Overview 95
4.2 Simulation results of a ventricular cardiac excitation using
rapid prototyping HDL Coder 95
4.2.1 Investigation of current – voltage (I-V) characteristics 96
4.2.1.1 FPGA-in-the-Loop of Voltage
Clamp Mechanism 102
4.2.2 Simulation results of LR-I using Simulink
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
x
floating-point 104
4.2.3 Simulation results of LR-I using Simulink
fixed-point 108
4.2.4 Comparison results between floating-point and
fixed-point methods 112
4.2.5 LR-I optimisation using HDL Coder 113
4.2.6 FPGA-in-the-Loop verification 120
4.3 Execution of cardiac excitation simulation on
FPGA board 122
4.3.1 The Xilinx FPGA Virtex-6 floor plan of the cardiac
excitation analysis system 122
4.3.2 FPGA-based on board simulation for cardiac
excitation analysis using ISim 124
4.3.3 Cardiac excitation on FPGA on-board simulation
using Chipscope Pro 126
4.4 Performance evaluation of the FPGA implemented LR-I
cardiac model simulation based analysis system 129
4.4.1 Accuracy evaluation: Simulation of cardiac
excitation response characteristics to periodic
trains of stimuli 129
4.4.2 Computational time performance evaluation 139
4.5 Summary 140
CHAPTER 5 CONCLUSIONS AND FUTURE WORKS 141
5.1 Overview 141
5.2 Achievements 142
5.3 Limitations 143
5.4 Future works 143
REFERENCES 146
APPENDIX A 157
APPENDIX B 162
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
xi
LIST OF PUBLICATIONS
Journal:
1. N. Othman, F. Mahmud, A. K. Mahamad, M. Hairol Jabbar, N. A. Adon,
“Voltage-clamp simulation of cardiac excitation: field programmable gate
array (FPGA) implementation,” ARPN Journal of Engineering and Applied
Sciences 2016. Vol. 11. no 24. pp. 14056-14064. ISSN 1819-6608.
International Conference Proceedings:
1. N. Othman, M. H. Jabbar, A. K. Mahamad, F. Mahmud, “Luo-Rudy Phase I
excitation modeling towards HDL coder implementation for real-time
simulation,” 5th International Conference on Intelligent and Advanced
Systems (ICIAS), 2014, pp.1-6, 3-5 June 2014.
2. N. Othman, F. Mahmud, A. K. Mahamad, M. Hairol Jabbar, N. A. Adon,
FPGA-in-the-Loop simulation of cardiac excitation modeling towards real-
time simulation. 5th
International Conference on Biomedical Engineering in
Vietnam (BME5), 2015. Vol. 46, pp. 266-269. Springer International
Publishing. ISBN: 978-3-319-11775-1
3. N. Othman, F. Mahmud, A. K. Mahamad, M. Hairol Jabbar, “Cardiac
excitation modeling: HDL coder optimisation towards FPGA stand-alone
implementation,” 2014 IEEE International Conference on Control System,
Computing and Engineering, pp. 507-511. ISBN: 978-1-4799-5685-2.
4. N. Othman, F. Mahmud, N. A. Adon, “FPGA In-the-Loop Simulation of
Cardiac Excitation Model under Voltage Clamp Conditions,” International
Conference on Engineering, Science and Nanotechnology 2016, Solo,
Indonesia, 3-5 Aug. 2016.
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
xii
5. N. A. Adon, F. Mahmud, M. Hairol Jabbar, N. Othman, “FPGA
implementation for cardiac excitation-conduction simulation based on
Fithugh-nagumo model,” Fifth International Conference on Biomedical
Engineering in Vietnam (BME5), 2014, pp.179-182, 16-18 June 2014.
6. N. A. Adon, F. Mahmud, M. Hairol Jabbar, N. Othman, “Optimisation in
MATLAB for cardiac excitation modeling towards FPGA standalone
simulation tools,” International Integrated Engineering Summit (IIES),
Applied Mechanics and Materials, 2014, Volume 773-774 1-4 Dec. 2014.
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
xiii
LIST OF TABLES
2.1 Action potential phase description 13
2.2 Comparison between experimental and simulations 22
2.3 Mathematical models to represent different regions
and species
23
2.4 Summary of ventricular mathematical model 24
2.5 Advantages of FPGA 31
2.6 Comparison of the processing speed of CPU and
FPGA
31
2.7 Comparison of Altera and Xilinx vendors 32
2.8 Comparison of different types of board produced
by Xilinx
33
2.9 Comparison of traditional method and rapid
prototyping method
46
2.10 Comparative study of development tools used in
the rapid prototyping method
47
2.11 Decision table of high performance applications 49
2.12 Comparison between FPGA and ASIC 50
2.13 Comparison FPGA and FPAA 53
2.14 Mathematical modeling of electrophysiology 56
3.1 Initial value of ODE variables 69
3.2 Ionic concentration 69
3.3 Constant value of maximum conductance, G 69
3.4 Constant value of Nernst Potential, E 70
3.5 Blocks for floating-point MATLAB Simulink 75
3.6 Comparison of data-type design between floating-
point, fixed-point (manual) and fixed-point with
Fixed-point Advisor tool
77
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
xiv
3.7 Modification blocks for fixed-point MATLAB
Simulink design
81
3.8 List of steps conducted in the HDL code
generation.
85
3.9 Supported boards for FPGA-in-the-Loop
verification
87
3.10 ISE Simulator (ISim) description 90
4.1 Summary of the FPGA performance for three types
of WL and FL
116
4.2 Results of pipelining optimisation with the
comparison between the design before and after
conducting the pipelining optimisation on Xilinx
Virtex-6 ML605 evaluation board
119
4.3 Comparison of computer simulations and hardware
implementations
139
4.4 Three comparisons of a single cell performance
evaluation based analog-hybrid and FPGA
140
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
xv
LIST OF FIGURES
1.1 Bar Chart of diseases that cause death in the
world
2
2.1 Overall literature 10
2.2 Electrical system of the heart 12
2.3 Action potential phases 13
2.4 Normal and abnormal heart rate 14
2.5 Emergent behaviour in cardiac-cell networks 15
2.6 stem cell is used to cure damaged heart of
mouse
18
2.7 Ionic current of Luo-Rudy Phase-I model 26
2.8 Luo-Rudy Phase I electrical circuit 26
2.9 Phase-locked of Luo-Rudy Phase I model 27
2.10 Field programmable gate array logic block,
switch block and Input/Output block (IOB)
layout
30
2.11 Xilinx FPGA Virtex-6 evaluation board 36
2.12 FPGA programming methods 38
2.13 Traditional method of FPGA programming 40
2.14 Comparison of rapid-prototyping method and
manual coding development time
41
2.15 dsPIC of Luo-Rudy Phase-I model for 80 cells 52
2.16 Timeline of simulation studies of
electrophysiology
55
3.1 The block diagram of research design 60
3.2 Overall process flow for the development of
Luo-Rudy Phase I (LR-I) cardiac model
simulation based analysis system using FPGA
72
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
xvi
3.3 Detail process flow for the development of
Luo-Rudy Phase I (LR-I) cardiac model
simulation based analysis system using the
FPGA
73
3.4 Summary of HDL Coder rapid prototyping
method
74
3.5 A window of Fixed-point Tool 79
3.6 A window of Fixed-point Advisor 79
3.7 A window of “add” parameter for fixed-point
setting
80
3.8 Data setting for lookup table 80
3.9 A window of HDL properties of “Sum” block
before inserting input and output pipelining
84
3.10 FPGA-in-the-Loop set up 86
3.11 Xilinx FPGA programming workflow 88
3.12 ISE Simulation (ISim) 90
3.13 Chipscope Pro version 14.6 starting window 91
3.14 Chipscope Pro set up 93
3.15 Process flowchart for realisation of on board
simulation using Chipscope Pro
93
4.1 Top level of Voltage clamp simulation of IK 96
4.2 MATLAB Simulink design block of
V_clamp_IK subsystem
97
4.3 Time-independent I-V characteristic waveforms
represents I-V characteristic of IK1
98
4.4 Time-independent I-V characteristic waveforms
represents I-V characteristic of IKp
99
4.5 Time-independent I-V characteristic waveforms
represents I-V characteristic of Ib
99
4.6 Time-dependent I-V characteristic waveforms
in response to various intensity of voltage step
inputs (from -60 mV to 80 mV) for an initial
holding voltage of -85 mV which represents the
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
xvii
I-V characteristic of INa 100
4.7 Time-dependent I-V characteristic waveforms
in response to various intensity of voltage step
inputs (from -60 mV to 80 mV) for an initial
holding voltage of -85 mV which represents the
I-V characteristic of IK
101
4.8 Time-dependent I-V characteristic waveforms
in response to various intensity of voltage step
inputs (from -60 mV to 80 mV) for an initial
holding voltage of -85 mV which represents the
I-V characteristic of Isi
101
4.9 voltage_clamp_of_IK_fil block 102
4.10 FIL results of voltage clamp of IK 103
4.11 Top level of block MATLAB Simulink for
Luo-Rudy Phase I model
104
4.12 Luo-Rudy Phase I (LR I) model by using
MATLAB Simulink
105
4.13 MATLAB Simulink Subsystem of Current Isi
(red dotted circle) of Luo-Rudy Phase I (LR-I)
model
106
4.14 Action potential for LR-I model 108
4.15 Fixed-point MATLAB Simulink of LR-I model 109
4.16 Top level of LR-I model in fixed-point
MATLAB Simulink
110
4.17 Action potential of LR-I for fixed-point with
Word Length and Fraction Length of (36,22)
110
4.18 VHDL code segment for Iext generated by the
HDL Coder from MATLAB Simulink
programming code
111
4.19 Comparison of Floating-point and Fixed-point
of LR-I design in MATLAB Simulink
112
4.20 Various of WL and FL of LR-I model 115
4.21 The difference between floating-point and
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
xviii
various word length (WL) and fraction length
(FL)
116
4.22 MATLAB Simulink of LR-I for pipelining
optimisation
118
4.23 Block generated for FPGA-in-the-Loop
verification
121
4.24 Results for FPGA-in-the-loop 121
4.25 Floor plan Ahead for LR-I model 123
4.26 ISim simulation 125
4.27 Cardiac analysis system through an FPGA on-
board simulation result displayed in Chipscope
Pro software
127
4.28 Complete result of cardiac excitation system-
on-board simulated by Chipscope Pro in
hexadecimal value and according to number of
sample
128
4.29 The result of a single-cell LR-I displayed by
Chipscope Pro after converted into decimal
value
128
4.30 Action potential of periodic stimulation current
with the intensity stimulation currents of -80
µA
132
4.31 Action potential of periodic stimulation current
with the intensity stimulation currents of -50
µA
134
4.32 Action potential of periodic stimulation current
with the intensity stimulation currents of -30
µA
136
4.33 Action potential of periodic stimulation current
with the intensity stimulation currents of -20
µA
138
A1 The FIL result for Isi 157
A2 The FIL result for INa 158
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
xix
A3 The FIL result for IK1 159
A4 The FIL result for IKp 160
A5 The FIL result for Ib 161
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
xx
LIST OF SYMBOLS AND ABBREVIATIONS
[Ca]i - inner cell calcium ion concentration
[K]i - inner cell potassium ionconcentration
[K]o - outer cell potassium ion concentration
[Na]i - inner cell sodium ion concentration
[Na]o - outer cell sodium ion concentration
∆t - time discretization step
C - membrane capacitance
clk_enb - input to start the system operation
d - activation gate of slow inward current
E - Nernst potential of ion channel
F - Faraday constant
f - inactivation gate of slow inward current
g - conductance of ion channel
G - maximum conductance of ion channel
h - inactivation gate of sodium
Ib - background current
Iext - external stimulation current
Iion - summation of all ion currents
IK - time-dependent potassium current
IK1 - time-independent potassium current
Ikp - time-independent plateau potassium current
Im - membrane current
INa - fast sodium current
Isi - slow inward current
j - inactivation gate of sodium
K1∞ - inactivation gate
Kp - inactivation gate
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
xxi
m - activation gate of sodium
PRNaK - permeability ratio
R - gas constant
T - absolute temperature
Vm - membrane voltage
Vmax - fast upstroke velocity
X - activation gate of time-dependent potassium
Xi - inactivation gate of time-dependent potassium
α - opening rate constants of gate
β - closing rate constants of gate
1-D - One-Dimensional
2-D - Two-Dimensional
3-D - Three-Dimensional
AP - Action Potential
APD - Action Potential Duration
ASCII - American standard code for information interchange
ASIC - Application Specific Integrated Circuits
AV - Atria ventricle
BER - Bit Error Rate
B-R
CiPA
-
-
Beeler and Reuter
Comprehensive In Vitro Proarrhythmia Assay
CLB - Configurable Logic Block
CM - Courtemanche
CMOS - Complementary metal oxide semiconductor
CORDIC - Coordinate Rotation Digital Computer
CPU - Computer Processing Unit
DAC - Digital Analog Converter
DEPE - differential equation processing element
DSP - Digital Signal Processing
dsPIC - Digital Signal Peripheral Interface Controller
EEPROM - Electrically Erasable Programmable Read-only Memory
FBDF - Agilent technologies fast binary data format
FF - Flip Flop
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
xxii
FHN - FitzHugh-Nagumo
FIL - FPGA-in-the-Loop
FL - Fraction length
FPAA - Field Programmable Analog Array
FPGA - Field Programmable Gate Array
FPU - Floating-point unit
GPP - General Purpose Processor
GPU - Graphical Processing Unit
GUI - Graphical User Interface
HDL - Hardware Description Language
I/O - Input/Output
IC - Integrated Circuit
ICON - Integrated Controller
ILA - Integrated Logic Analyser
IOB - Input/Output Block
ISE - Integrated Software Environment
ISim - ISE Simulator
I-V - Current-Voltage
JTAG - Joint test action group
LAB - Logic Array Block
LC - Logic Cell
LE - Logic Element
LR-I - Luo-Rudy Phase I
LUT - Look-up Table
MHz - Mega Hertz
MSE - Mean Squared Error
MUX - Multiplexer
NCD - Native Circuit Description
NGD - Native Generic Database
ODE - Ordinary Differential Equations
PAR - Place and Route
PC - Personel Computer
PCI - Peripheral Component Interconnect
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
xxiii
PE - Percentage Error
PE - Processing Element
RAM - Read-only Memory
RK-4 - Runge-Kutta forth order
ROM - Random-access Memory
RTL - Register Transfer Level
SA - Sinoatrial
SIPHER - Scalable Implementation of Primitives for Homomorphic Encryption
SNR - Signal to Noise Ratio
SoC - System-on-Chip
SVPWM - space vector pulse width modulation
UCF - user constraint file
USB - Universal Serial Bus
VCD - value change dump
VHDL - Very High Speed Integrated Circuit (VHSIC) Hardware Description
Language
VHM - Virtual Heart Model
VIO - Virtual Input/Output
VLSI - Very Large Scale Integration
WL - Word length
XSG - Xilinx System Generator PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
xxiv
LIST OF APPENDICES
APPENDIX TITLE PAGE
A The FIL results of the I-V characteristics
for the ionic channels in the Luo-Rudy
Phase I model
157
B The full VHDL programming code for the
Luo-Rudy Phase I model based analysis
system
162
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
CHAPTER 1
INTRODUCTION
1.1 Overview
This thesis examines the simulation study of a cardiac cell mathematical model on
hardware implementation. Specifically, this study is to improve understanding of a
cardiac excitation mechanism by reproducing quantitatively the action potential
generation and phase-locked response to periodic current pulse stimulation by using
high performance Field Programmable Gate Array (FPGA) implementation for Luo-
Rudy Phase I (LR-I) model.
Section 1.2 discussed on the research background of cardiac excitation, while,
section 1.3 summarised the problem statement that has been reported by previous
studies which include large scale of variables, massive amounts of computational
time, and challenges in writing the Hardware Description Language (HDL) code
manually that lead to error prone, time consuming and high level languages that are
difficult to be understood by non-FPGA experts, and the solution to these problems
also are proposed. Besides, section 1.4 presents the research objectives, while section
1.5 explained the research scope and limitations. Lastly, the overall research
contribution is discussed in section 1.6 and the thesis organisation is presented in 1.7.
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
2
1.2 Research background
Since 2014, heart disease has been announced as the top major killer cause of death
in the world issued by the World Health Organization (WHO) [1]. Referring to the
Figure 1.1, the seven critical diseases in the world are heart disease, stroke,
diarrhoeal disease, HIV/AIDS, malaria, lung cancer and diabetes. According to
World Health Statistics 2014, the ischaemic heart disease has the highest percentage
which is 28.6% compared to the other critical diseases in the world as illustrated in
Figure 1.1 that shows the seven types of diseases that cause death in the world [1].
Moreover, cardiovascular diseases had killed 17.5 million people that are three in
every ten deaths annually. Of these, 7.4 million people died of ischaemic heart
disease and 6.7 million from the stroke.
16.1 17.928.6
6.4 6.9 8.9 15.4
Diarrhoeal disease
Stroke
Ischemic heart disease
Diabetes
Lung cancer
Malaria
Human immunodeficiency virus (HIV)/Acquired immunodeficiency syndrome (AIDS)
Percentages of critical diseases in the world
Figure 1.1: Bar Chart of diseases that cause death in the world
Acccording to Figure 1.1, the ischemic heart disease leads the chart. Ischemic
heart disease is a heart problem caused by heart blockages in arteries that are
narrowed. This will cause less blood and oxygen reaches the heart muscle and can
produce cardiac arrest [2]. The cardiac arrest is known as any disorder that causes the
abrupt loss of heart function in a person who may or may not has diagnosed heart
disease [3]which caused by the abnormalities in the cardiac electrical system such as
arrhythmia [4]. However, the mechanism of the arrhythmias is difficult to
understand. This situation leads to two techniques that have been used widely to
study the underlying mechanism of the heart known as experimental and simulations
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
3
techniques. The experimental technique can be divided into two types which are in-
vitro and in-vivo. While, the simulation technique also can be categorized into two
types which are computer and hardware simulations.
In-vitro refers to the experimental manipulation conducted using cell-free
extracts and purified or partially purified biomolecules in test tubes. Generally, the
in-vitro done in a laboratory environment using test tubes, Petri dishes [5].
Meanwhile, in-vivo research refers to the characterization and analysis of
biomolecules and biological systems in the context of intact organisms. Studies that
are in-vivo are those in which the effects of various biological entities are tested on
whole, living organisms, usually animals, including humans, and plants as opposed
to a partial or dead organism [6].
In-vitro and in-vivo studies are used to first build and then verify
computational modeling which allowing integration of past discovery, quantitative
computation of the models and the projection across relevant spatial and temporal
scales [7]. However, the in-vitro technique has some drawbacks which are in-vitro
needs of high variables quantity for monitoring, high-resolution data in investigating
larger proportions and also costly [8]. Meanwhile, computer simulations are utilised
for plausibility assessment, hypothesis generation and prediction which defining
further in-vitro research targets [7].
Advancement growth in mathematical modeling of cardiac cells through
mathematical descriptions of electrical events at the cellular level and its computer
simulation has contributed to use simulations as a tool for studying the cardiac
dynamics. Furthermore, the computer simulation approach helps in reducing and
replacing the use of animals in the cardiac research [9]. Therefore, many
mathematical models related to excitable media have been developed to represent
different regions of the cardiac [10] such as Hodgkin – Huxley [11], [12], FitzHugh-
Nagumo (FHN) model [13], Noble Purkinje model [14], Beeler and Reuter [15], and
Luo-Rudy ventricular model [16] and [17]. Furthermore, Priebe-Beukelmann (PB)
[18] that consists of 22 ODE variables, Ten Tusscher-Noble-Panfilov [19] model
which consists of 17 ODE variables, and Iyer-Mazhari-Winslow [20] model that
contain 67 ODE variables also have been developed in order to model the cell in
more detail.
Thus far, the mathematical model becomes more advanced from year to year
as variable parameters increased in order to represent the model in more detail.
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
4
Hence, this situation causes a new problem which needs a long time to compute the
mathematical model. This scenario also gives the problems with the computer
simulation method which required a fast speed computational computer, such as
supercomputer in order to perform the simulations and also raise the costs. The fact
of this issue has been convinced by Dr. Jeremy Rice a computational physiologist at
IBM’s Thomas J. Watson Research Center (2012) [21]:
“A heart simulation requires a computer with the ability to track the individual cells
and their interactions between cells, up to 10,000 times per second for each heart
beat”
Due to this weakness of computer simulation, most of the researchers preferred
simple models and not to use the latest detailed models [10].
Alternatively, high performance and low power consumption hardware
simulation provide valuable tools for electrophysiological applications such as in the
medical and educational field. Currently, the researchers have moved to hardware
simulation of analysis tool of cardiac electrophysiology considering their advantages
of extremely fast and parallel mode execution, low power usage, reconfigurable,
development ease and low cost [22], [23]. One way to achieve a reduction in the
power consumption and size is by implementing the design using Very Large Scale
Integration (VLSI) technology [24]. For example, hardware tools that can be used for
electrophysiological applications are Digital Signal Processing (DSPs), Field
Programmable Gate Arrays (FPGAs), Graphical Processing Unit (GPU) and
Application Specific Integrated Circuits (ASICs) [25]. Meanwhile, with the
reliability requirements of biomedical instruments, FPGA embedded system
development showing a trend of growth [26]. FPGA technology is now considered
very useful by an increasing number of designers in various fields of application as it
offers flexible, reconfigurable hardware, programmable circuit architecture, execute
in parallel mode with million gate counts, and low power consumption [22].
Moreover, it is also suitable for solving higher orders of Ordinary Differential
Equations (ODEs) and high performance for real-time applications [27].
Therefore, the aim of this study is to reproduce quantitatively action potential
generation and phase-locked response to periodic current pulse stimulation by using
high performance FPGA implementation for Luo-Rudy Phase I (LR-I ) model with
small physical size and low power consumption. The LR-I model described by a set
of nonlinear first order ODEs that includes eight dynamic state variables for
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
5
describing six types of ion channel currents [28]. In this research, the LR-I model is
designed using MATLAB Simulink for rapid prototyping in order to match the
algorithms with FPGA hardware implementation towards a real-time simulation in
producing an analysis system to study the underlying mechanism of the cardiac
through understandings of non-linear dynamics in cardiac excitation. The simulation
results obtained using the FPGA board is then compared to those obtained
numerically in LR-I model to verify the accuracy and the performance of
computational time, power consumption, maximum frequency and area. The FPGA
implementation of the cardiac mathematical model simulations contributes in
accelerating the electrophysiology simulation to achieve real-time simulations of the
cardiac mathematical model.
1.3 Problem statement
Towards better and quantitative understandings of electrophysiological mechanisms
of the cardiac, mathematical models of cardiac cells have been developed by many
researchers in order to simulate action potentials in a variety of conditions, where the
action potential provides a basis of the electrophysiological function of the cardiac
through the cardiac excitation-contraction mechanism [12], [15], [16], [13].
However, it is inevitable for those models to become large scale in the number of
dynamical variables, requiring immense amounts of computational time for their
dynamic simulations [7], [8], [29]. Therefore, a high performance system, in terms of
fast speed execution and low power consumption hardware has been essential and
very important [7], [8], [30] aspect to make the models useful for understanding
complex system of the cardiac. The existing hardware that had been developed has
high power consumption and large physical size [31]. Therefore, the Field
programmable Gate Array (FPGA) is the most suitable solution for the high
performance system as it executes in parallel operation to achieve real-time
simulations, low power consumption and small size [18]. Although Digital Signal
Processing (DSP) and Application Specific Integrated Circuit (ASIC) also greatly
offer the ability to simulate in real-time simulations, they are lacking in terms of
power and high cost compared to FPGA. Nevertheless, the designing process
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
6
involves FPGA expertise in Very High Speed Integrated Circuit (VHSIC) Hardware
Description Language (VHDL) code. By manually written the VHDL Code, it gives
the disadvantages as it is error prone, time consuming and high level languages that
difficult to understand to a non-expertise on FPGA [32]. Therefore, using an FPGA
rapid prototyping method through the MATLAB Hardware Description Language
(HDL) Coder, the software application from Mathworks offers automatic HDL code
generation from MATLAB Simulink design and the code verification by using the
FPGA-in-the-Loop (FIL) approach. Therefore, the intention in the present study is to
perform high performance simulations of the cellular excitations of the cell models
based on the LR-I model using the FPGA rapid prototyping method to design a
hardware model responsible for the cellular excitations.
1.4 Research objectives
The objectives of this research are summarised as follows;
1. To construct a Luo-Rudy Phase I (LR-I) model algorithm based on Hardware
Description Language (HDL) Coder for an FPGA implementation.
2. To conduct a real-time simulation based analysis technique using a Field
Programmable Gate Array (FPGA) displayed on Chipscope Pro software for
the LR-I model based cardiac excitation.
3. To verify the accuracy of the simulation results and the computational time
performance of the technique with conventional computer simulation method
based on studies of cellular process in a cardiac excitation.
1.5 Research scope
This research is not focused on the design architecture of the Field Programmable
Gate Array (FPGA). However, this research is concerned more about the
implementing the Nonlinear Ordinary Differential Equation (ODE)-based
mathematical model on the FPGA for simulation assisted cardiac excitation analysis.
Therefore, the design of the Luo-Rudy Phase I (LR-I) model based cardiac excitation
analysis system is constructed by using MATLAB Simulink and MATLAB
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
7
Hardware Description Language (HDL) Coder rapid prototyping method in order to
generate a synthesisable Very High Speed Integrated Circuit (VHSIC) Hardware
Description Language (VHDL) code for faster development.
Besides, FPGA-in-the-Loop (FIL) approach provided by the HDL Coder is
used to verify the designed MATLAB Simulink blocks implemented on Xilinx
FPGA Virtex-6 evaluation board. Lastly, the generated code by HDL Coder is
modified, synthesised and implemented by using Xilinx Integrated Software
Environment (ISE) Design Suite 14.6 software on the FPGA. The simulation using
the constructed FPGA system is conducted on the Chipscope Pro which is capable to
log data for further analysis.
The LR-I model is chosen for this research as it is the most favourable model
among researchers for cardiac cell and it also provides enough fundamental ionic
currents in order to understand the dynamic mechanism of the cardiac [11].
1.6 Overall contributions
This research is focusing on the Field Programmable Gate Array (FPGA) approach
for a real-time simulation based cardiac analysis that provides high performance in
simulating the electrophysiological mechanism of the cardiac. In addition, the rapid
prototyping approach by using Hardware Description Language (HDL) Coder is used
to provide a fast prototype development for solving Ordinary Differential Equation
(ODE). The FPGA implementation in the cardiac model simulation for the cardiac
excitation analysis is essential in improving simulation performance in terms of the
speed and power consumption for the better understanding of the heart
electrophysiological mechanisms related to cardiac arrhythmia disease treatment and
management.
1.7 Thesis organisation
The thesis organisations are as follows. Chapter 2 concentrates on the most recent
studies related to mathematical models and deep explanations of Luo-Rudy Phase-I
(LR-I) mathematical model. Besides, the previous studies related to real-time
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
8
simulations of electrophysiology on Field Programmable Gate Array (FPGA) and
other hardware platforms based also being discussed in Chapter 2.
In Chapter 3, the methodology and design strategies for the LR-I model based
cardiac excitation analysis system using MATLAB for FPGA implementation are
described. The development of LR-I model simulation based analysis system using
FPGA include FPGA design using HDL Coder rapid prototyping method and FPGA
programming on Xilinx FPGA Virtex-6 evaluation board is also discussed in Chapter
3.
The simulation results obtained from the LR-I model based cardiac excitation
analysis system is discussed in Chapter 4. The discussions of the results are based on
the simulation results of a ventricular cardiac excitation using rapid prototyping
Hardware Description Language (HDL) Coder, execution of cardiac excitation
simulation on the FPGA evaluation board and performance evaluation of the LR-I
cardiac model simulation based analysis system using the FPGA. Comparison in
terms of the accuracy of simulation results and computational time performance
between a conventional computer simulation using MATLAB and the FPGA
hardware implementation are discussed in Chapter 4.
Lastly, in Chapter 5, the concluding remarks and future works are
highlighted. Future suggestions for implementing the FPGA for the mathematical
model are presented to improve the system performance in terms of the design
system, analysis scope and complex model selection also stated in Chapter 5. PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
9
CHAPTER 2
LITERATURE REVIEW
2.1 Overview
In this study, certain keywords have been used to review the related works which is
electrical system of the heart, techniques to study underlying mechanism of the heart
include experimental, clinical and simulations, cardiac mathematical modeling, two
types of mechanism involved in cellular level of cardiac cell which are phase-locked
and voltage clamp, hardware used for real-time simulations and details about the
Field Programmable Gate Array (FPGA) as visualised in Figure 2.1. The pink
coloured items (hardware implementation, Luo-Rudy Phase I, FPGA, Xilinx Virtex-
6, and HDL Coder) are referred to the mainly focus topics that have been used in this
project and will be further discussed in section 2.4 until 2.7.
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
10
Lite
ratu
re R
evi
ew
Elec
tric
al S
yste
m
of
the
Hea
rt
Tech
niq
ues
of
card
iac
elec
tro
ph
ysio
logi
cal a
nal
ysis
Car
dia
c m
ath
emat
ical
m
od
elin
g
Exp
erim
enta
lSi
mu
lati
on
s
Co
mp
ute
rH
ard
war
e
Ven
tric
ula
r ca
rdia
c m
ath
emat
ical
mo
del
Luo
Ru
dy
Ph
ase-
I m
od
el
Car
dia
c el
ectr
op
hys
iolo
gy
at c
ellu
lar
leve
l
Ph
ase-
lock
ed
Vo
ltag
e cl
amp
Har
dw
are
Imp
lem
enta
tio
n
Tech
niq
ues
ASI
C, G
PU
. DSP
, D
SPIC
, FP
AA
an
d
FPG
A
FPG
A
Xili
nx
Vir
tex-
6
FPG
A
Pro
gram
min
g m
eth
od
HD
L C
od
er
Act
ion
po
ten
tial
an
d c
ard
iac
exci
tati
on
Xili
nx
Syst
em
Gen
erat
or
DSP
Bu
ilder
Cat
apu
ltC
In-v
itro
In-v
ivo
Lab
Vie
w
Fig
ure
2.1
: O
ver
all
lite
ratu
re
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
11
2.2 Electrical system of the heart
A cardiac excitation occurs when ions of sodium, potassium, calcium and chloride
flowing through ion channels in and out of the plasma membrane that generates
currents and causes changes in membrane potential of the cell from resting to action
potential (AP). In a definition, an AP is the electrical signal that passes through the
excitable cell when it excites. Commonly, the AP is triggered by a voltage spike
from the AP of its neighbouring tissue or from an artificial pacing signal. The cardiac
AP is a short-lasting event in which the membrane potential of a cardiac cell rises
and falls following a consistent trajectory. For normal cardiac conditions, the
electrical excitation wave dies when it reaches a complete activation of myocardium
[33]. On the other hand, for abnormal conditions, the propagating wave does not die
out completely, but re-excite the myocardium that has recovered from refractoriness
which can cause arrhythmia diseases and lead to a sudden cardiac death [34].
The purpose of the electrical system of the heart is to coordinate the pumping
of the four chambers of the heart and to control the heart rate so that the heart speeds
up and slows down as the demands of the body change. The electrical system of the
heart as illustrated in Figure 2.2 maintains blood circulation of the body by rhythmic
contractions of the atria and the ventricles. The coordination of the cardiac
contraction is achieved through a coordinate conduction of the AP electrical signal in
the heart. The electrical signal arises from tissue in the sinoatrial (SA) node, which
presents as the primary pacemaker of the heart as shown in Figure 2.2(a). The
electrical signal produced by the SA node is conducted radially through both atria,
causing them to contract as shown in Figure 2.2(b). The electrical signal then travels
through the atria causing them to contract, down through the atrio-ventricular (AV)
node located between the atria and the ventricles, continues down, first through the
bundle of His which separates into the right and left bundle as depicted in Figure
2.2(c). Then, the signal passes through the slow conducting atria ventricle (AV)
node, allowing blood to empty out of the atria and fill the ventricles as depicted in
Figure 2.2(d). The electricity then flow out to the muscle fibers of the ventricles
through the Purkinje fibers which are the final “thin wires” that spread the signal
through the muscle fibers of the ventricles as shown in Figure 2.2(e). As the impulse
spreads, the muscles contract and the ventricles pump as shown in Figure 2.2(f) [35].
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
12
Depolarization
Repolarization
SA node
AV
node
P
R
QS
T
P
R
QS
T
P
R
QS
T
P
R
QS
T
P
R
QS
T
P
R
QS
T
1. Atrial depolarization, initiated byThe SA node, causes the P wave
2. With atrial depolarization complete,
the impulse is delayed at the AV node
2. With atrial depolarization complete,
the impulse is delayed at the AV node
3. Ventricular depolarization begins at apex, causing the QRS complex. Atrial
repolarization occurs
4. Ventricular depolarization is complete
5. Ventricular repolarization begins at
apex, causing the T wave
6. Ventricular repolarization is complete
Depolarization Repolarization
SA node
AV
node
P
R
QS
T
P
R
QS
T
P
R
QS
T
P
R
QS
T
P
R
QS
T
P
R
QS
T
1. Atrial depolarization, initiated byThe SA node, causes the P wave
2. With atrial depolarization complete,
the impulse is delayed at the AV node
2. With atrial depolarization complete,
the impulse is delayed at the AV node
3. Ventricular depolarization begins at apex, causing the QRS complex. Atrial
repolarization occurs
4. Ventricular depolarization is complete
5. Ventricular repolarization begins at
apex, causing the T wave
6. Ventricular repolarization is complete
Depolarization Repolarization
(d)
(e)
(f)
(a)
(b)
(c)
Figure 2.2: Electrical system of the heart
https://www.mmrl.edu/cardiac-arrhythmia/
Generally, the AP has five phases which are Phase 0, Phase 1, Phase 2, Phase 3 and
Phase 4 as illustrated in Figure 2.3 [34], [36]. A detailed description of each phase is
summarised in Table 2.1.
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
13
Figure 2.3: Action potential phases
Table 2.1: Action potential phase description
2.3 Mechanism of Cardiac Arrhythmia
The arrhythmia is an abnormal rhythm of the heart which refers to any change from
the normal sequence of electrical impulses. The electrical impulses may happen too
fast, too slowly, or erratically, which causing the heart to beat slowly, faster or
Phases Description
Phase 0 – upstroke It is characterised by a sharp, tall upstroke of the action potential. The cell
receives an impulse from neighboring cell and depolarises. During this
phase, the cell depolarises and begins to contract.
Phase 1 – spike Contraction occurred. The cell begins an early, rapid, partial depolarization.
Phase 2 – plateau
Contraction completes, and the cell begins relaxing. This is a prolonged
phase of slow repolarization. This plateau phase of the cardiac action
potential is sustained by a balance between inward movement of Ca2+
(ICa)
through L-type calcium channels and outward movement of K+ through the
slow delayed rectifier potassium channels, IKs.
Phase 3 – downslope The L-type Ca
2+ channels close, while the slow delayed rectifier (IKs) K
+
channels are still open. This is the final phase of rapid repolarization.
Repolarization is complete by the end of the phase 3.
Phase 4 – rest This is the cells resting phase. The cell is ready to receive an electrical
stimulus. Na+
and Ca2+
channels are closed at rest transmembrane voltage.
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
14
irregularly. Generally, there are two types of arrhythmia which are bradyarrhythmia
and tachyarrhythmia. Bradyarrhythmia happens when the heart rate is less than 60
beats per minute. While tachyarrhythmia occurs when the heart rate is more than 100
beats per minute. Figure 2.4 shows the normal condition, bradyarrhythmia and
tachyarrhythmia conditions.
Normal (60-100 beats/minute)
Bradyarrhythmia (below 60 beats/minute)
Tachyarrhythmia (over 100 beats/minute)
Figure 2.4: Normal and abnormal heart rate [37]
The arrhythmia can affect how well the heart works. The heart may not be
able to pump enough blood to meet the body's needs and completely harmless or life-
threatening. Therefore, the electrophysiological study is important in order to
understand the mechanism for cardiac rhythm management and treatment [38]. Until
now, the methodologies used to understand the non-linear dynamics of the cardiac
are experimental, clinical, and mathematical model simulations[7], [18], [39].
2.4 Techniques of cardiac electrophysiology analysis
Generally, there are two types of techniques to study the underlying mechanisms and
dynamic characteristics of cardiac cells which are experimental and simulation
techniques. Besides, the model simulation technique can be divided into two types
which are computer simulation and hardware implementation. This subtopic will be
discussed further details on these two methods regarding experimental (in-vitro and
in-vivo) and simulation (software and hardware) techniques.
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
15
Figure 2.5 visualises the results from previous studies using computer
simulation and experimental for cardiac electrophysiology which is denoted as (a) for
electrocardiogram, (b) and (c), for two-dimensional (2-D) mappings from a
simulation and experimental techniques, respectively of an electrical activity in
cardiac ventricles occurring in a normal, tachycardia and fibrillation conditions [37].
Ventricular tachycardia shows the appearance of multiple frequencies driven by
spiral waves of electrical activity (polymorphic tachycardia), with subsequent
deterioration of a chaotic signal known as fibrillation. The blue to orange colours and
black to grey colours in the figure, respectively indicate the action potential range of
values from -85 mV (negative) to 20 mV (positive).
(a)
(b)
(c)
-85 mV
20 mV
Simulation
Experiment
-85 mV
20 mV
Figure 2.5: Emergent behaviour in cardiac-cell networks. (a) electrocardiogram, (b)
and (c) simulation and experimental mappings of spiral waves of electrical activity
occurring in the cardiac during tachycardia and fibrillation, respectively [37]
2.4.1 Experimental technique
2.4.1.1 In-vitro technique
An in-vitro involves the experimentation outside whole living organism in a
controlled laboratory condition [40]. In-vitro enables the focused evaluation of the
cellular responses of the cardiomyocytes to physiologic and pharmacologic events
under precisely controlled conditions. The probability is high that the results are
clinically applicable if subsequent in animal myocardial studies yield results similar
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
16
to those obtained with human cell culture studies [41]. Generally, the in-vitro
involves the used of test tubes and petri dish which placed all the solutions needed in
the experiment. The in-vitro also provides a convenient method to evaluate a variety
of potential interventions which can then be tested in animal models [9]. The case
studies of the in-vitro details are discussed as follows.
In 2016, the research had been conducted to analyse the cardiac safety
screening to evaluate the propensity of drugs to produce QT interval prolongation
and arrhythmia. The Comprehensive In Vitro Proarrhythmia Assay (CiPA) was
developed to update the existing cardiac safety testing paradigm to better evaluate
arrhythmia risk and remove the need for thorough QT wave studies. The
Comprehensive In Vitro Proarrhythmia Assay (CiPA) approach produced a
standardised ion channel assay approach, incorporating defined tests against major
cardiac ion channels, the results of which then inform evaluation of proarrhythmic
actions in silico, using human ventricular action potential reconstructions. Results are
then to be confirmed using human (stem cell–derived) cardiomyocytes. The CiPA
approach leads to improved and widely accepted cardiac safety testing guidelines
[42].
In 2015, the new microelectrode array device named as PerFlexMEA which
enabled controlled coupling between myocytes and nonmyocytes was used in
cardiovascular conduction studies. The device consists of an 8 μm thin parylene
microporous membrane with a 4 × 5 microelectrode array patterned on one side.
Myocytes and nonmyocytes can be plated on either side of the parylene membrane to
create a tissue bilayer. The packaged PerFlexMEA was fited in a 60 mm culture dish
and recording experiments were performed by simply plugging it into a
commercially available multielectrode amplifier system where the recorded signals
were processed and analysed using scripts generated in MATLAB. The experimental
results had provided evidence of the reliability of this device, as conduction velocity
was observed to decrease after inducing lateral hetero-cellular controlled coupling
between myocytes and HeLaCx43 cells [43].
Although the in-vitro technique was used to learn about a human disease or to
predict the safety of new drugs in many areas such as stem cells and post-marketing
drug surveillance, this technique is very time consuming due to small parameters can
be studied at one time. Besides, it is expensive in terms of materials and equipment
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
17
to conduct the experiment and it is not represent the true nature of the real organism
which may effect the results’s accuracy.
2.4.1.2 In-vivo technique
The in-vivo technique involves the experimentation using a whole living organism
such as mice and rabbit where drugs are directly injected into the body [40]. For
example, the pressure catheters allows invasive hemodynamic measurements and
accurate detection of cardiac function. Pressure-volume analysis allows pre- and
afterload-independent hemodynamic studies and measurement of cardiac
contractility through in-vivo and it is suitable for studying cardiac signaling
pathways and drug testing [44].
In 2001, the researcher used stem cells to replace damaged heart cells and
literally restore cardiac function [45]. This work suggested that injured heart cells
can shift from a quiescent state into active cell division. In this study, a heart attack
was induced in a mice by tying off a major blood vessel, the left main coronary
artery. Through the identification of unique cellular surface markers, the
investigators then isolated a select group of adult primitive bone marrow cells with a
high capacity to develop into cells of multiple types. When the stem cells were
injected into the damaged wall of the ventricle, these cells led to the formation of
new cardiomyocytes, vascular endothelium, and smooth muscle cells, thus
generating the novo myocardium, including coronary arteries, arterioles, and
capillaries as shown in Figure 2.6. The newly formed myocardium occupied 68 % of
the damaged portion of the ventricle nine days after the bone marrow cells were
transplanted, in effect replacing the dead myocardium with living, functioning tissue.
The researchers found that the mice that received the transplanted cells survived in
greater numbers than the mice with heart attacks that did not receive the mouse stem
cells.
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
18
Figure 2.6: Stem cell is used to cure damaged heart of mouse [45]
However, this approach is not quite suitable to be used due to it could cause
various risk assessment procedures to the patient, has limited sources of data and has
uncertainty or less informative with respect to the long-term performance of the
device [46]. Furthermore, the results of in-vivo testing and measurement may
significantly influence to lack of explanations of the underlying mechanisms in
cardiac electrophysiology. Therefore, a simulation technique is used to avoid the
limitations of the in-vivo study and to solve the above issues. Through the
simulation, a better understanding of the normal and abnormal condition of cardiac
electrical activity at various levels, such as in the ion channels, cells, tissues and
organ could be achieved.
2.4.2 Simulation technique
The creation of cardiac models shows the efforts aimed to enhance the understanding
of the underlying mechanism of the heart and predict behaviour in various normal
and abnormal conditions of the cardiac. The development of such models have been
driven by several factors, including;
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
19
i) Precise in-vitro and in-vivo data collection. This has been important to
construct models based on real data and the models are verified.
ii) Increasingly more complex computers such as super computer to process
and compile the simulations.
iii) The use of the models and simulations themselves to improve the
understanding of the underlying mechanisms of cardiac arrhythmia and
used to predict responses under conditions that are sometimes hard to
reproduce in-vitro or in-vivo preparations.
Besides, simulations can be divided into two types which are computer and
hardware simulations. The detail reviews regarding both methods are discussed in
the section 2.4.3.1 and 2.4.3.2.
2.4.2.1 Computer
Computer simulations referred to the cardiac simulation are performed on computer
softwares such as Visual Studio software by using C++ Programming Language,
MATLAB Simulink by using graphical programming blocks, and etc. Recent
advancements in computational science and the development of high-performance
computers have increased the usage of the computer simulation technique in order to
study the underlying mechanism of the heart. The computer simulation is the most
favourable technique as it enables the creation of multi-scale simulation by using the
cardiac models.
Since the pioneering work of Noble [47], numerical simulation has been
recognised as a powerful and indispensable tool for understanding the
electrophysiology of the cardiac [48]. By using this technique, the biological effect
can be represented by several known equations, so virtual human organs and virtual
metabolism programs can predict drug effects in humans more accurately than
animals in order to design the molecular structure of drugs to target specific receptors
[9]. For example, the protease inhibitors for patients with HIV were designed by
computer and computer models, bypassing animal tests due to the urgent need for a
treatment. In 1997, Roche Pharmaceuticals had a new cardiac drug approved on the
strength of data from a virtual cardiac because the animal data were inconclusive.
Furthermore, the scientists can simulate experiments in silico (on computer) in
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
20
minutes that could take months or years to do in the lab or clinic [9] and computer
simulations also decrease the number of animals used, capable of obtaining the
results quickly comparable to experimental technique, reduce cost, and very flexible
to control the variables.
However, the computer simulations require huge computational resources,
thus computational efficiency becomes a prime concern [48]. For example,
simulation time for one second of excitation in the whole atrium using the
Courtemanche (CM) model [49] was around 18.7 hours [48]. In addition, the
research also reported that the single cardiac cycle required about six hours of
computer simulations [50]. In 2011, the K-computer (supercomputer) developed by
RIKEN and Fujitsu, comprising 864 computer racks equipped with a total of 88,128
Computer Processing Unit (CPUs), has achieved the world’s highest LINPACK
which is a software library for performing numerical linear algebra on digital
computers benchmark performance (10.51 petaflops). Petaflops is a measure of
computer performance that referred to 1015
floating-point operations per second.
Even at this level of performance, a whole cardiac model based on the dynamics of
each molecule in the myocyte could not be accomplished [48]. Therefore, a new
solution needs to be introduced to achieve real-time simulations of the model and
hardware implementation promising great advantages such as low power
consumptions and parallel mode execution that lead to high performance and real-
time system.
2.4.2.2 Hardware
Hardware simulation represents here is the real-time simulation of cardiac excitation
model that is adapted to the hardware such as Field Programmable Gate Array
(FPGA), Field Programmable Analog Array (FPAA), Graphical Processing Unit
(GPU), Digital Signal Processing (DSP), Application Specific Integrated Circuit
(ASIC), Digital Signal Peripheral Interface Controller (dsPIC) and etc. Recently, in
2015, the hardware is widely chosen by researchers as the computational tool to
study the underlying mechanism of the cardiac since it provides faster execution time
compare to computer simulation according to it’s real-time and parallel mode
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
21
execution [50]. Furthermore, they commonly require very low power consumption in
their operation with a lower cost compared to the supercomputer.
In 2007, a research had been carried out by J. Zhao and Y.B. Kim [51] to
build a simple neuron model, the FitzHugh-Nagumo (FHN) model, was implemented
on FPAAs. The differential equations of the model were integrated by making
arithmetic operations on the reconfigurable voltage model circuits of the FPAAs.
Based on the results, it was able to realise neuron dynamics in real-time and therefore
provided a low-cost, high performance, and dynamical reconfigurable analog circuit
solution.
Meantime, in 2012, a research had been conducted by F. Mahmud [31] which
proposed a hardware-implemented cardiac excitation model of a cardiac cell based
on Luo-Rudy phase I (LR-1) for the action potential (AP) generation in a mammalian
cardiac ventricle. The hardware-implemented cardiac excitation model was designed
by using analog circuits and a digital signal Peripheral Interface Controller (dsPIC)
microcontroller that could reproduce time-dependent and time-independent nonlinear
current-voltage characteristics of six-type of ionic currents in the LR-1 model. Based
on the results, real-time simulations of reentrant excitation conduction of cardiac
cells were realised by coupling 80 active circuits of the cell models based on a cable
model. The real-time simulations of initiation have been performed by the model and
they are comparable to those performed by a conventional computer simulation.
Thus, it is conceivable that the hardware-implemented cardiac excitation model may
be useful as one of the alternative tools to further understanding of the reentrant
mechanisms.
Furthermore, in 2015, Nouri et. al., presented a set of piecewise linear FHN
models, which can reproduce different behaviours, similar to the biological neuron
[52]. Nouri et. al., presented a set of equations as a model to describe the
mechanisms of a single neuron, which were implementable on FPGA. Simulation
results showed that the model can reproduce different behaviours of the neuron.
Then, the proposed models were investigated, in terms of digital implementation
feasibility and computational overhead, targeting low-cost hardware realization and
had shown that the proposed models through hardware synthesis and physical
implementations on the FPGA can produce a range of neuron behaviours with higher
performance and lower implementation cost compared to the original model.
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
22
2.4.3 Comparison between experimental and simulations techniques
As mentioned previously in Section 2.4.1 and 2.4.2, there are two types of techniques
to study the underlying mechanism of the cardiac, which are experimental and
simulations. According to Table 2.2, it can be noticed that experiments have several
limitations which are limited studies parameters, surface recording only and unable
to perform in large scale due to the high cost of the animal cell. Meanwhile,
simulation of cardiac models has adequate/sufficient parameters studies as they are
able to perform in large numbers of parameter studies and they only depend on the
specification of computer and hardware that will be used for the simulation and
implementation. Besides, using software and hardware simulations, the models are
able to be performed on a large scale, such as 1-Dimension (1-D) and up to 3-
Dimension (3-D). While, by performing the cardiac cell model using the hardware, a
real-time simulation can be achieved compared to computer simulation which needs
a vast amount of computational time to conduct the simulation.
Table 2.2: Comparison between experimental and simulations
Types of Studies Experimental
Simulations
Computer
Simulation
Hardware
Implementation
Study parameters limited Adequate/Sufficient
Surface recording
observations √ X
1D-3D, Large scale X √
Cellular process and
the dynamics of action
potential
Qualitative Quantitative
Cost High Higher cost in
supercomputing Low
Simulation time Not related Time-consuming Real-time
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
23
2.5 Cardiac mathematical modeling
The mathematical models of cardiac excitation have produced a greater
understanding of how the cardiac muscle contracts and how they are used for
simulation-based analysis in electrophysiology field studies [48]. The models are
designed based on the cardiac regions and species [11] as stated in Table 2.3. In the
cardiac electrophysiology studies, they are focused on the electrical activity of the
cardiac under both normal and abnormal conditions. Many of the studies are
critically done on the ventricle part since abnormal processes such as arrhythmia
usually originate from this region, thus many ventricular cell excitation models have
been developed.
Table 2.3: Mathematical models to represent different cardiac regions and species
Mathematical model Regions Species ODE
Hunter et al., 1976 [53] Purkinje Mammalian 1
Beeler and Reuter, 1977 [54] Ventricle Mammalian 8
Luo and Rudy, 1991 [16] Ventricle Guinea pig 8
Endresen, 1997 [55] Sino-atrial node Mammalian 3
Inada et al., 2009 [56] Atrio-ventricular
node Mammalian 29
Li et al., 2010 [57] Ventricle Mouse 36
Recently, the mathematical models are greatly increased in term of number
of variables, complex equations, and higher order of integration as well as higher
order Ordinary Differential Equations (ODEs) [39] thus, this requires fast
computational speed to complete the simulations which contributed to the usage of
the high cost supercomputer for the analysis [48]. Therefore, a new solution which is
through the hardware implementation needs to be introduced to analyse various
electrophysiological mechanisms of the cardiac using these models.
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
24
2.5.1 Ventricular cardiac mathematical modeling
Table 2.4 shows the various ventricular mathematical models that had been
developed by researchers as reported by Noble [11]. The first ventricular cardiac cell
model that had been established is Krause et al. which used the mammalian cardiac
cell. Beeler & Reuter model was developed that contains eight Ordinary Differential
Equations (ODEs) later and in 1991, the most well-known model named as Luo-
Rudy Phase-I (LR-I) model was established by using eight ODEs for the guinea pig
model. The advancement of the model is increasing in the number of ODEs in order
to represent the model in more details, for an example, the Noble et. al had further
developed the model of ventricle for the guinea pig that used 17 of ODEs in 1991.
The latest ventricular cell model developed by Ten Tusscher and Panfilov are based
on experimental human data for most of the main ionic currents included slow
delayed rectifier currents (IKs) and L-type calcium current (ICaL) [58].
LR-I model is chosen for this research as it is the most favourable model
among researchers for cardiac cell as can be seen via the citations in the Table 2.4
and it also provides enough fundamental ionic currents which is eight ionic currents
in order to understand the dynamic mechanism of the cardiac cell behaviour [16].
Table 2.4: Summary of ventricular mathematical model
Model Species ODEs Citations Comments
Krause et al.
1966 [59] Mammalian - -
First ventricular
cardiac cell model
Beeler &
Reuter, 1977
[54]
Mammalian 8 411 First well-used
ventricular model
Luo & Rudy,
1991 [16] Guinea pig 8 619
First well-described
ventricular model
Noble et al.,
1991[60] Guinea pig 17 85
First well-described
ventricular model-
based on LR-I
Iyer et al.,
2004[20] human 67 -
Very long
computational time
[58]
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
146
REFERENCES
[1] World Health Organization. World Health Statistics 2014. Switzerland: WHO
Library catalogue. 2014.
[2] Plunkett, S. Cardiovascular Emergencies. Emergency Procedures for the
Small Animal Veterinarian. 2006. 206(1): 1-56.
[3] Swedberg, K., Cleland, J. ,Dargie, H., Drexler, H., Follath, F., Komajda, M.,
Tavazzi,L., Smiseth,O. A., Gavazzi, A., Haverich, A., Hoes, A., Jaarsma, T.,
Korewicki, J, Lévy, S., Linde,C., Lopez-Sendon,J. L., Nieminen, M. S.,
Piérard, L. and Remme,W. J. Guidelines for the diagnosis and treatment of
chronic heart failure: Executive summary. European Heart Journal. 2005.
26(11):1115-1140.
[5] Douglas P. Z. and Hein J. J. W. Sudden Cardiac Death. Circulation. 1998.
98(1):2334–2351.
[6] Edmunds, S. J. The Effects Of Kiwifruit Extracts On Gene And Protein
Expression Levels In In-Vitro And In-Vivo Mouse Models Of Inflammatory
Bowel Disease. Ph.D. Thesis. The University of Auckland; 2010.
[7] Farcasiu, A. T., Farcasiu, C., sescu, L. A. T., Andrei, O. C., Stef. D. M. N.
and Munteanu,A. In-vitro and in-vivo evaluation of two surface treatments on
a commercial denture base biomaterial. Proc. of The 5th IEEE International
Conference on E-Health and Bioengineering. Lasi, Romania: Grigore T. Popa
University of Medicine and Pharmacy. 2015. pp. 31–34.
[8] Quinn, T. A. and Kohl, P. Combining wet and dry research: experience with
model development for cardiac mechano-electric structure-function studies.
Cardiovasc. Research. 2013. 97(4): 601–611.
[9] Mahmud, F. Real-time Simulation of Cardiac Excitation Using Hardware-
implemented Cardiac Excitation Modeling. International Journal of
Integrated Engineering. 2012. 4(3): 13-18.
[10] Arora, T., Mehta, A. K., Joshi, V., Mehta, K. D., Rathor, N., Mediratta, P. K.
and Sharma, K.K. Substitute of Animals in Drug Research: An Approach
Towards Fulfillment of 4R’s. Indian journal of pharmaceutical sciences.
2011. 73(1):1–6.
[11] Mahmud, F. Real-Time Simulation and Control of Spatio-Temporal Cardiac
Excitation using an Analog-Digital Hybrid Circuit Model. Ph.D. Thesis.
Osaka University; 2011.
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
147
[11] Noble, D., Garny, A. and Noble P. J. How the Hodgkin-Huxley equations
inspired the Cardiac Physiome Project. J.Physiol. 2012. 590(11): 2613-2628.
[12] Usyk, T. P. and McCulloch, A. D. Relationship between regional shortening
and asynchronous electrical activation in a three-dimensional model of
ventricular electromechanics. J. Cardiovasc. Electrophysiol. 2003. 14(10):
196-202.
[13] Kerckhoffs, R. C., Faris, O. P., Bovendeerd, P. H., Prizen, F. W., McVeigh, E.
R. and Arts, T. Electromechanics of paced left ventricle simulated by
straightforward mathematical model: comparison with experiments. American
Journal of Physiology. Heart and Circulatory physiology. 2005. 289(5): 889-
897.
[15] Kerckhoffs, R. C. P., Bovendeerd, P. H. M., Kotte, J. C. S., Prinzen, F. W.,
Smits, K. and Arts, T. Homogeneity of cardiac contraction despite
physiological asynchrony of depolarization: A model study. Ann. Biomed.
Eng. 2003. 31(5): 536-547.
[16] Seemann, G., Sachse, F. B., Weiss, D. L. and Dössel, O. Quantitative
reconstruction of cardiac electromechanics in human myocardium: regional
heterogeneity. J. Cardiovasc. Electrophysiol. 2003. 14(10): 219-228.
[17] Luo, C. H. and Rudy, Y. A model of the ventricular cardiac action potential.
Depolarization, repolarization, and their interaction. Circ. Research. 1991.
68(6): 1501-1526.
[18] C. H. and Rudy, Y. A dynamic model of the cardiac ventricular action
potential. I. Simulations of ionic currents and concentration changes. Circ.
Research. 1994. 74(6): 1071-1096.
[19] Priebe, L. and Beuckelmann, D. J. Simulation Study of Cellular Electric
Properties in Heart Failure. Circ. Research. 1998. 82(11): 1206–1223.
[20] Ten Tusscher, K. H., Noble, D., Noble, P. J. and Panfilov, A. V. A model for
human ventricular tissue. American Journal of Physiology. Heart and
Circulatory Physiology. 2004. 286(4): 1573-1589.
[21] Iyer, V., Mazhari, R. and Winslow, R. L. A computational model of the
human left-ventricular epicardial myocyte. Biophys. Journal. 2004. 87(3):
1507-1525.
[22] Mirin, A. A., Richards, D. F., Glosli, J. N., Draeger, E. W., Chan, B.,
Fattebert, J. L., Krauss, W. D., Oppelstrup, T., Rice, J. J., Gunnels, J. A.,
Gurev, V., Kim, C., Magerlein, J., Reumann, M. and Wen, H. F. Toward real-
time modeling of human heart ventricles at cellular resolution: Simulation of
drug-induced arrhythmias. Proc. of the Int. Conf. for High Perform.
Computing, Networking, Storage and Analysis (SC). Salt Lake City, Utah.
2012. pp. 1-11.
[23] Joost, R. and Salomon, R. Advantages of FPGA-based multiprocessor systems
in industrial applications. Proc. of the 31st Annual Conference of IEEE
Industrial Electronics Society. North Carolina, USA. 2005. pp. 445-450.
[24] Alfke, P. Virtex-6 and Spartan-6, plus a look into the future. Proc. of the
International Conference on Field Programmable Logic and Applications.
Prague. 2009. pp. 5-5.
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
148
[25] Wijekoon, J. H. B. and Dudek, P. Spiking and bursting firing patterns of a
compact VLSI cortical neuron circuit. Proc. of the Int. Conf. Neural
Networks. Orlando, Florida. 2007. pp. 1332-1337.
[26] Dong, C., Ieong, C. I., Vai, M. I., Mak, P. U., Mak, P. I. and Wan, F. A real-
time heart beat detector and quantitative investigation based on FPGA. Proc.
of the Asia Pacific Conference on Postgraduate Research in Microelectronics
and Electronics (PrimeAsia). Macau, China. 2011. pp. 65-69.
[27] Matsubara, T. and Torikai, H. Asynchronous Cellular Automaton-Based
Neuron: Theoretical Analysis and On-FPGA Learning. IEEE Transactions on
Neural Networks and Learning Systems. 2013. 24(5): 736-748.
[28] Alireza, F., Trong, T. D., Chedjou, J. and Kyamakya, K. New computational
modeling for solving higher order ODE based on FPGA. Proc. of the 2nd
International Workshop on Nonlinear Dynamics and Synchronization.
Klagenfurt, Austria. 2009. pp. 49-53.
[29] Fenton, F. H. Cherry, E. M. Hastings, H. M. and J. Evans,S. Multiple
mechanisms of spiral wave breakup in a model of cardiac electrical activity.
Chaos. 2002. 12(3): 852-892.
[30] Southern, J., Pitt-Francis, J., Whiteley, J., Stokeley, D., Kobashi, H., Nobes,
R., Kadooka, Y. and Gavaghan, D. Multi-scale computational modelling in
biology and physiology. Prog. Biophys. Mol. Biology. 2008. 96(1-3): 60-89.
[31] Higham, J., Aslanidi, O. and Zhang, H. Large speed increase using novel GPU
based algorithms to simulate cardiac excitation waves in 3D rabbit ventricles.
Proc. of the Computing in Cardiology. Hangzhou, China. 2011. pp. 9–12.
[32] Mahmud, F., Shiozawa,N., Makikawa, M. and Nomura, T. Reentrant
excitation in an analog-digital hybrid circuit model of cardiac tissue. Chaos.
2011. 21(2): 1-15.
[33] Town, G. E.and Siwakoti, Y. P. Design of FPGA-controlled power electronics
and drives using MATLAB Simulink. Proc. of the ECCE Asia Downunder
(ECCE Asia). Melbourne, Australia. 2013. pp. 571–577.
[34] Cabo, C. and Rosenbaum, D. S. Quantitative Cardiac Electrophysiology.
Florida, USA. CRC Press- Taylor & Francis Group LLC. 2002.
[35] John, R. M., Tedrow, U. B., Koplan, B. A., Albert C. M., Epstein, L. M.,
Sweeney, M. O., Miller, A. L., Michaud, G. F. and Stevenson, W.G.
Ventricular Arrhythmias and Sudden Cardiac Death. Lancet. 2012. 380(9852):
1520–1529.
[36] Zhan, H., Xia, L. and Huang, R. Effects of the fibroblast-myocyte in cardiac
electromechanical coupling: A preliminary simulation study. Proc. of the
Computing in Cardiology. Hangzhou, China. 2011. pp. 69-72.
[37] Abedin, Z. and Conner, R. Essential Cardiac Electrophysiology with self-
assessment. USA: Wiley-Blackwell. 2008.
[38] Bartocci, E., Cherry, E. M., Glimm, J., Grosu, R., Smolka, S. A. and Fenton,
F. H. Toward real-time simulation of cardiac dynamics. Proc. of the 9th
International Conference on Computational Methods in Systems Biology. New
York, USA. 2011. pp. 103–112.
[39] Redaelli, A. A Model of Health: Mathematical Modeling Tools Play an
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
149
Important Role in Optimizing New Treatment Options for Heart Disease. A
Magazine of the IEEE Engineering in Medicine and Biology Society. Stanford
Soc. Innov. Rev. USA. 2015. pp. 27–32.
[40] Carusi, A., Burrage, K. and Rodríguez, B. Bridging experiments, models and
simulations: an integrative approach to validation in computational cardiac
electrophysiology. American Journal Physiology Heart Circ. Physiology.
2012. 303(2): 144–155.
[40] Major Differences. (2013). Difference between in vivo and in vitro. Retrieved
on January 2, 2017. from
http://www.majordifferences.com/2013/02/difference-between-invivo-and-
invitro.
[41] Tumiati, L. C., Mickle,D. A. G., Weisel, R. D., Williams,W. G. and Li,R. K..
An in vitro model to study myocardial ischemic injury. J. Tissue Cult.
Methods. 1994. 16(1):1–9.
[42] Fermini, B., Hancox, J. C., Abi-Gerges, N., Bridgland-Taylor, M., Chaudhary,
K. W., Colatsky, T., Correll, K., Crumb, W., Damiano, B., Erdemli, G.,
Gintant,G., Imredy, J., Koerner, J., Kramer, J., Levesque, P., Li, Z., Lindqvist,
A., Obejero-Paz, C. A., Rampe, D., Sawada, K., Strauss, D. G. and
Vandenberg, J. I.. A New Perspective in the Field of Cardiac Safety Testing
through the Comprehensive In Vitro Proarrhythmia Assay Paradigm. J.
Biomol. Screen.. 2016. 21(1):1–11.
[43] Mondal, A., Baker, B., Harvey, I. R. and Moreno,A. P.. PerFlexMEA: A thin
microporous microelectrode array for in vitro cardiac electrophysiological
studies on hetero-cellular bilayers with controlled gap junction
communication. Lab Chip. 15(1):2037–2048.
[44] Zhang L. (2014). In vivo cardiac function. Retrieved on January 2, 2017. from
http://www.iemf.no/science/in_vivo/
[47] Kirschstein, R. Stem cells: scientific progress and future research directions.
USA: Department of Health and Human Services, Institute of Health. 2001.
[46] Kim, J., Shapiro, L. and Flynn, A. The clinical application of mesenchymal
stem cells and cardiac stem cells as a therapy for cardiovascular disease.
Pharmacol. Ther.. 2015. 151(1): 8–15.
[48] Noble, D. A Modification of the Hodgkin-Huxley Equations Applicable to
Purkinje Fibre Action and Pace-Maker Potentials. J. Physiology. 1962. 160(2):
317–352.
[49] Sugiura, S., Washio, T., Hatano, A., Okada, J., Watanabe, H. and Hisada, T.
Multi-scale simulations of cardiac electrophysiology and mechanics using the
University of Tokyo heart simulator. Prog. Biophys. Mol. Biology. 2012.
110(2-3): 380–389.
[50] Courtemanche, M., Ramirez, R. J. and Nattel, S. Ionic mechanisms underlying
human atrial action potential properties : insights from a mathematical model
Ionic mechanisms underlying human atrial action potential properties :
insights from a mathematical model. The American Journal of Physiology.
1998. 275(1 pt 2): 301-321.
[51] Siegle, J. H., Hale, G. J., Newman, J. P. and Voigts, J. Neural ensemble
communities : open-source approaches to hardware for large-scale
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
150
electrophysiology. Curr. Opin. Neurobiology. 2015. 32(1): 53–59.
[52] Zhao, J. and Kim, Y. B. Circuit implementation of FitzHugh-Nagumo neuron
model using Field Programmable Analog Arrays. Proc. of the 50th Midwest
Symposium on Circuits and Systems. Montreal, Que. 2007. pp. 772-775.
[53] Nouri, M. Karimi, G. R. Ahmadi, A. and Abbott, D. Digital multiplierless
implementation of the biological FitzHugh–Nagumo model. Neurocomputing.
2015. 165(1): 1–9.
[54] Hunter, P. J., McNaughton, P. A. and Noble,D. Analytical models of
propagation in excitable cells. Prog. Biophys. Mol. Biol. 1976. 30(23): 99–
144.
[55] Beeler, G. W. and Reuter, H. Reconstruction Of The Action Potential Of
Ventricular Myocardial Fibres. Journal of Physiology. 1977. 268(1): 177–210.
[56] Endresen,L. P. Chaos in weakly-coupled pacemaker cells. J. Theor. Biology.
1997. 184(1): 41–50.
[57] Inada, S., Hancox, J. C., Zhang, H. and Boyett,M. R. One-dimensional
mathematical model of the atrioventricular node including atrio-nodal, nodal,
and nodal-His cells. Biophys. J.. 2009. 97(8): 2117–2127.
[58] Li, L., Niederer, S. A., Idigo, W., Zhang, Y. H., Swietach, P., Casadei, B. and
Smith, N. P. A mathematical model of the murine ventricular myocyte: a data-
driven biophysically based approach applied to mice overexpressing the
canine NCX isoform. Am. J. Physiol. Heart Circ. Physiol.. 2010. 299(4):
H1045–H1063.
[59] Carro, J., Rodriguez, J. F., Laguna, P. and Pueyo, E. A human ventricular cell
model for investigation of cardiac arrhythmias under hyperkalaemic
conditions. Philos. Trans. R. Soc. A Math. Phys. Eng. Science. 2011.
369(1954): 4205–4232.
[60] Krause, H., Antoni, H. and Fleckenstein, A. An electronic model for the
formation of local and transmitted stimuli on the myocardium fibers based
upon variable current-voltage characteristics for potassium and sodium ions.
Pflugers Arch Gesamte Physiol Menschen Tiere. 1966. 289(1): 12-36.
[61] Noble, D., Noble, S. J., Bett, G. C. L., Earm, Y. E., Ho, W. K. and So, I. K.
The role of sodium-calcium exchange during the cardiac action potential.
Annals of the New York Academy of Sciences. 1991. 639(1): 334–353.
[62] Shaw, R. M. and Rudy, Y. Ionic Mechanisms of Propagation in Cardiac
Tissue : Roles of the Sodium and L-type Calcium Currents During Reduced
Excitability and Decreased Gap Junction Coupling. Circ. Research. 1997.
81(5): 727–741.
[63] Bueno-Orovio, A. Cherry, E. M. and Fenton, F. H. Minimal model for human
ventricular action potentials in tissue. J. Theor. Biology. 2008. 253(3): 544–
560. [64] Guevara, M., Shrier, R. A. and Glass, L. Phase-locked rhythms
in periodically stimulated heart cell aggregates. American Journal of
Physiology. 1988. 254(1 Pt 2): 1–10.
[65] Murthy, A., Bartocci, E., Fenton, F. H., Glimm, J., Gray, R. A., Cherry, E. M.,
Smolka, S. A. and Grosu, R. Curvature analysis of cardiac excitation
wavefronts. IEEE/ACM Trans. Comput. Biol. Bioinformatics. 2013. 10(2):
323–336.
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
151
[66] Wilders, R. Dynamic clamp: a powerful tool in cardiac electrophysiology. J.
Physiology. 2006. 576(Pt 2): 349–359.
[67] Tanaka, H., Komikado, C., Shimada, H., Takeda, K. and Namekata, I. The R
(-) -Enantiomer of Efonidipine Blocks T-type but Not L-type Calcium Current
in Guinea Pig Ventricular Myocardium. J. Pharmacol. Science. 2004. 96(4):
499–501.
[68] Banyasz, T., Horvath, B., Jian, Z., Izu, L. T. and Chen-Izu, Y. Profile of L-
type Ca2+ current and Na+/Ca2+ exchange current during cardiac action
potential in ventricular myocytes. Heart Rhythm. 2012. 9(1): 134–142.
[69] Mahmud, F., Akuhana, T. S., Hiozawa, N. S. and Omura, T. N. An Analog-
Digital Hybrid Model of Electrical Excitation in a Cardiac Ventricular Cell.
Cardiac Ventricular Cell. Trans. Jpn. Soc. Med. Biol. Engineering. 2009.
47(5): 428–435.
[70] Ou, K., Rao, H., Cai, Z., Guo, H., Lin, X., Guan, L., Maguire, T., Warkentin,
B. and Chen, Y. MMC-HVDC Simulation and Testing Based on Real-Time
Digital Simulator and Physical Control System. IEEE Journal of Emerging
and Selected Topics in Power Electronics. 2014. 2(4): 1109–1116.
[71] Shi, Y. and Monti, A. Notice of Violation of IEEE Publication Principles
FPGA-based fast real time simulation of power systems. Proc. of the Power
and Energy Society General Meeting - Conversion and Delivery of Electrical
Energy in the 21st Century. Pittsburgh, PA. 2008. pp. 1-5.
[72] Wang, Y. and Schaefer, U. Real Time Simulation of a FPGA Based Space
Vector PWM Controller. Proc. of the International Symposium on Power
Electronics, Electrical Drives, Automation and Motion. Pisa, Italy. 2010. pp.
833–838.
[73] Dinavahi, V. and Chen, Y. Multi-FPGA digital hardware design for detailed
large-scale real-time electromagnetic transient simulation of power systems.
IET Generation, Transmission & Distribution. 2013. 7(5): 451–463.
[74] Dufour, C. Abourida, S. and Belanger, J. Real-Time Simulation of Permanent
Magnet Motor Drive on FPGA Chip for High-Bandwidth Controller Tests and
Validation, Proc. of the IEEE International Symposium on Industrial
Electronics. Montreal, Que. 2006. pp. 2591–2596.
[75] Scekic, O. and Milutinovic, V. FPGA Comparative Analysis. School of
Electrical Engineering, University of Belgrade. 2005. pp. 1-48.
[76] Xilinx.Virtex-6 Family Overview. USA: Product specification. 2012.
[77] Xilinx. Defense-Grade 7 Series FPGAs Overview. USA: Product
specification. 2013.
[78] Xilinx. DSP48E1 Slice User Guide. USA: Product specification. 2011.
[79] Xilinx. Satisfying The insatiable demand for higher bandwidth. Interfaces
(Providence). USA: Product specification. 2009.
[80] Huang, C., Vahid, F. and Givargis, T. A Custom FPGA Processor for Physical
Model Ordinary Differential Equation Solving. IEEE Embedded Systems
Letters. 2011. 3(4): 113-116.
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
152
[81] Matar, M. and Iravani, R. FPGA Implementation of the Power Electronic
Converter Model for Real-Time Simulation of Electromagnetic Transients.
IEEE Transactions on Power Delivery. 2010. 25(2): 852-860.
[82] Sousa, T. Dataflow Programming Concept, Languages and Applications.
Doctoral Symposium on Informatics Engineering Conference. Porto. 2012. pp.
1-12.
[83] Amornwongpeeti, S., Ono, N. and Ekpanyapong, M. Design of FPGA-based
rapid prototype spectral subtraction for hands-free speech applications. Proc.
of the Conference on the Signal and Information Processing Association
Annual Summit. Siem Reap. 2014. pp. 1-6.
[84] Kintali B. K., and Gu,Y. Model-Based Design for Altera FPGAs Using
Simulink , HDL Coder , and Altera DSP Builder Advanced Blockset. 2014. pp.
1-11.
[85] Mathworks. HDL Coder. USA: Product specification. 2014.
[86] Butt, S. A. Lavagno, L. and Torino, P. Model-based rapid prototyping of multi
rate digital signal processing algorithms. Proc. of the NORCHIP. Lund. 2011.
pp. 1-4.
[87] Ristovi, M., Lubura, S. and Jokic, D. Implementation of Cordic Algorithm on
FPGA Altera Cyclone. Proc. of the 20th Telecommunications Forum
(TELFOR). Belgrade. 2012. pp. 875–878.
[88] Cousins, D. B., Rohloff, K., Peikert, C. and Schantz, R. An update on SIPHER
(Scalable Implementation of Primitives for Homomorphic EncRyption) –
FPGA implementation using Simulink. Proc. of the IEEE Conference on High
Performance Extreme Computing. Waltham, MA. 2012. pp. 1-5.
[89] Sarode, S., Radhakrishnan, S., Sampath, V., Jiang, Z., Pajic, M. and
Mangharam, R. Demo Abstract: Model-Based Testing of Implantable Cardiac
Devices. Proc. of the Third International Conference on Cyber-Physical
Systems. Beijing, China. 2012. pp. 221–221.
[90] Shi, T., Rui, G., Zhang, Y. and Zhang, S. Design Method for Duffing System
Based on DSP Builder. Proc. of the International Conference on Systems and
Informatics. Yantai. 2012. pp. 121-124.
[91] Zengchui, M. and Xin, W. Design and Realization of Real-Time Spectrum
Analysis System Based on DSP Builder. Proc. of the Second International
Conference on Instrumentation, Measurement, Computer, Communication and
Control. Harbin. 2012. pp. 1077–1080. [92] Beun, R., Karkowski I. and
Ditzel, M. C++ Based Design Flow for Reconfigurable Image Processing
Systems. Proc. of the International Conference on Field Programmable Logic
and Applications. Amsterdam. 2007. pp. 571-575.
[93] Reddy, B. N. K., Suresh, N., Ramesh, J. V. N., Pavithra, T., Bahulya, Y. K.,
Edavoor, P. J. and Ram, S. J. An Efficient Approach for Design and Testing of
FPGA Programming using LabVIEW. Proc. of the International Conference
on Advances in Computing, Communications and Informatics. Kochi, India.
2015. pp. 543–548.
[94] Beeck, V. K., Heylen, F., Meel, J. and Geodeme, T. Comparative study of
Model-based hardware design tools. Proc. of the ECUMICT. Belgium. 2010.
pp. 1-12.
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
153
[95] Maslennikow, O. and Sergiyenko, A. Mapping DSP algorithms into FPGA.
Proc. of the Int. Symp. Parallel Comput. Electr. Engeneering. Bialystok,
Poland. 2006. pp. 208–213.
[96] L. Adams. Choosing the Right Architecture for Real-Time Signal Processing
Designs. Dallas,Texas: White Paper. 2002.
[97] Xu, F., Chen, H., Jin, W. and Xu, Y. FPGA implementation of nonlinear
model predictive control. The 26th Chinese Control and Decision Conference
(2014 CCDC). Changsha, China. 2014. pp. 108-113.
[98] Cope, B., Cheung, P. Y. K., Luk, W. and Howes, L. Performance Comparison
of Graphics Processors to Reconfigurable Logic: A Case Study. IEEE Trans.
Comput.. 2010. 59(4): 433–448.
[99] Tian, X. and Benkrid, K. High-performance quasi-monte carlo financial
simulation: FPGA vs. GPP vs. GPU. ACM Transactions on Reconfigurable
Technology and Systems. 2010. 3(4): 1–19.
[100] Li, X., Wu, J., Yu, Z., Xu, C. and Chen, K. An Adaptive GPU Performance
and Power Model. Proc. of the 4th IEEE International Conference on
Information Science and Technology. Shenzhen, China. 2014. pp. 665-669.
[101] Kordasiewicz, R. C. and Shirani, S. ASIC and FPGA implementations of
H.264 DCT and quantization blocks. Proc. of the 12th IEEE International
Conference on Image Processing. Italy. 2005. pp. III-1020-3.
[102] Kuon, I. and Rose, J. Measuring the gap between FPGAs and ASICs. IEEE
Trans. Comput. Des. Integr. Circuits Systems. 2007. 26(2): 203–215.
[103] Patlolla, D. R., Breeding, E.. Jones, W. F. and Everman, J. A GPU-based
architecture for improved online rebinning performance in clinical 3-D PET.
Proc. of the IEEE Nuclear Science Symposium Conference Record
(NSS/MIC). Orlando, Florida. 2009. pp. 3135-3139.
[104] Asano, S., Maruyama, T. and Yamaguchi, Y. Performance comparison of
FPGA, GPU and CPU in image processing. Proc. of the 19th International
Conference on Field Programmable Logic and Applications. Prague. 2009.
pp. 126–131.
[105] Fratta, A., Griffero, G. and Nieddu, S. Comparative analysis among DSP and
FPGA-based control capabilities in PWM power converters. 30th Annual
Conference of IEEE Industrial Electronics Society. Busan, South Korea. 2004.
pp. 257–262.
[106] Bucolo, M., Caponetto, R., Dongola, G., Gallo, A. and Sapuppo, F. An FPGA
based approach for nonlinear characterization of electrocardiographic data.
Proc. of the International Symposium on Industrial Electronics. Bari, Italy.
2010. pp. 1567-1572.
[107] Datta, A., Debbarma, S., Mukherjee D. and Saha, H. A dsPIC based efficient
single-stage grid-connected photovoltaic system. Proc. of the IEEE Region 10
Conference. Bangkok, Thailand. 2014. pp. 1-4.
[108] Uriz, A. J., Moreira, J. C., Hidalgo, R., Aguero, P., Tulli, J. C. and Gonzalez,
E. An Implementation in dsPIC of a Denoising Algorithm Based on the
Discrete Wavelet Transform. Proc. of the Andean Region International
Conference. Cuenca, Ecuador. 2012. pp. 199–202.
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
154
[109] Mahmud, F. Real-time Simulations for Resetting and Annihilation of
Reentrant Activity Using Hardware-implemented Cardiac Excitation
Modeling. Proc. of the IEEE-EMBS Proceeding of International Conference
on Biomedical Engineering & Sciences (IECBES). Langkawi, Malaysia. 2012.
pp. 321–325.
[110] Selow, R. Lopes, H. S. and Lima, C. R. E. A comparison of FPGA and FPAA
technologies for a signal processing application. Proc.of the International
Conference on Field Programmable Logic and Applications. Prague. 2009.
pp. 230–235.
[111] Zairi, H., Kedir -Talha, M., Benouar S., and Ait -Amer, A., Intelligent system
for detecting cardiac arrhythmia on FPGA. Proc. of the 5th International
Conference on Information and Communication Systems (ICICS). Irbid,
Jordan. 2014. pp. 1-5.
[112] Luo, C. H., and Rudy, Y. A dynamic model of the cardiac ventricular action
potential. II. Afterdepolarizations, triggered activity, and potentiation. Circ.
Research. 1994. 74(6): 1097–1113.
[112] Mahajan, A., Shiferaw, Y., Sato, D., Baher, A., Olcese, R., Xie, L.-H.,
Yang,M.-J., Chen, P.-S., Restrepo, J. G., Karma, A., Garfinkel, A., Qu,Z. and
Weiss,J. N.. A rabbit ventricular action potential model replicating cardiac
dynamics at rapid heart rates. Biophys. J.. 2008. 94(2):392–410.
[113] Maeda, Y., Yagi, E. and Makino, H. Synchronization with low power
consumption of hardware models of cardiac cells. BioSystems. 2005. 79(1-3):
125–131.
[114] Ten Tusscher, K. H., Bernus, O. Hren, R. and Panfilov, A. V. Comparison of
electrophysiological models for human ventricular cells and tissues. Prog.
Biophys. Mol. Biology. 2006. 90(1-3): 326–345.
[115] Ros, E., Ortigosa, E. M., Agís, R., Carrillo, R. and Arnold, M. Real-time
computing platform for spiking neurons (RT-spike). IEEE Trans. Neural
Networks. 2006. 17(4): 1050–1063.
[116] Cherry, E. M., and Fenton, F. H. A tale of two dogs: analyzing two models of
canine ventricular electrophysiology. American Journal of Physiology, Heart
and Circulatory Physiology. 2007. 292(1): 43–55.
[117] Rachmuth, G. and Poon, C.S. Transistor analogs of emergent iono-neuronal
dynamics. HFSP Journal. 2008. 2(3): 156–166.
[118] Korhonen, T., Hänninen,S. L. and Tavi, P. Model of excitation-contraction
coupling of rat neonatal ventricular myocytes. Biophys. J.. 2009. 96(3):1189–
1209.
[118] Vigmond, E. J., Boyle, P. M., Leon, L. J. and Plank, G. Near-real-time
simulations of biolelectric activity in small mammalian hearts using graphical
processing units. Proc. of the Annual International Conference of the IEEE
Engineering in Medicine and Biology Society. Minneapolis, MN. 2009. pp.
3290-3293.
[120] G. S. B. Williams, G. D. Smith, E. a. Sobie, and M. S. Jafri, “Models of
cardiac excitation-contraction coupling in ventricular myocytes,” Math.
Biosci., vol. 226, no. 1, pp. 1–15, 2010.
[120] Amorim, R. M., Rocha, B. M., Campos, F. O., and Santos, R. W. Automatic
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
155
code generation for solvers of cardiac cellular membrane dynamics in GPUs.
Proc. of the Annual International Conference of the IEEE Engineering in
Medicine and Biology Society. Buenos Aires, Argentina. 2010. pp. 2666–
2669.
[121] Wong, J., Göktepe, S. and Kuhl, E. Computational modeling of
electrochemical coupling: A novel finite element approach towards ionic
models for cardiac electrophysiology. Computer Methods in Applied
Mechanics and Engineering. 2011. 200(45-46): 3139–3158.
[122] Wang, W., Huang, H. H., Kay, M. and Cavazos, J. GPGPU Accelerated
Cardiac Arrhythmia Simulations. Proc. of the 33rd Annual International
Conference of the IEEE Engineering in Medicine and Biology Society. Boston,
MA, USA. 2011. pp. 724–727.
[123] Roberts, B. N., Yang, P.-C., Behrens, S. B., Moreno, J. D. and Clancy, C. E.
Computational approaches to understand cardiac electrophysiology and
arrhythmias. AJP Hear. Circ. Physiology. 2012. 303(7): H766–H783.
[124] Dal, H., Göktepe, S., Kaliske,M. and Kuhl, E. A fully implicit finite element
method for bidomain models of cardiac electromechanics. Comput. Methods
Appl. Mech. Engeneering. 2013. 253(1): 323–336.
[125] Berberoğlu, E., Solmaz, H. O. and Göktepe, S.. Computational modeling of
coupled cardiac electromechanics incorporating cardiac dysfunctions. Eur. J.
Mech. - A/Solids. 2014. 48(1): 1–14.
[126] Gomar, S. and Ahmadi, A. Digital Multiplierless Implementation of
Biological Adaptive-Exponential Neuron Model. IEEE Transactions on
Circuits and Systems I: Regular Papers. 2014. 61(4): 1206–1219.
[127] Beeler, G. W. and Reuter, H. Reconstruction of the action potential of
ventricular myocardial fibres. J. Physiology. 1977. 268(1): 177–210.
[128] Howard, P. Solving ODE in MATLAB. Undergraduate Thesis. Univ. Maryl;
2007.
[129] Dormand, J. R. and Prince, P. J. A family of embedded Runge-Kutta formulae.
J. Comput. Appl. Math.. 1980. 6(1): 19–26.
[130] Kiusalaas, J. Numerical Methods in Engineering with MATLAB. United
Kingdom. Cambridge University Press. 2005. pp.
[131] Wang, Q., Kanemasa, Y., Li, J., Jayasinghe, D., Shimizu, T., Matsubara, M.,
Kawaba, M. and Pu, C. Detecting Transient Bottlenecks in n-Tier
Applications through Fine-Grained Analysis. Proc. of the 33rd International
Conference on Distributed Computing Systems. Philadelphia, PA. 2013. pp.
31–40.
[132] Zhang, Z., Anantharam, V., Wainwright, M. J., and Nikolić, B., An efficient
10GBASE-T Ethernet LDPC decoder design with low error floors. IEEE
Journal of Solid-State Circuits. 2010. 45(4): 843-855.
[133] Wang, C. C., Shi, C., Brodersen, R. W. and Markovi, D. An Automated Fixed-
Point Optimization Tool in MATLAB XSG / SynDSP Environment. ISRN
Signal Processing. 2011. pp. 1–17.
[134] Hager, S., Winkler, F., Scheuermann, B. and Reinhardt, K. Building
Optimized Packet Filters with COFFi. Proc. of the 22nd Annual International
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
156
Symposium on Field-Programmable Custom Computing Machines. Boston,
MA, USA. 2014. pp. 105–105.
[135] Schwall, M. and Jondral, F. K. High-speed turbo equalization for GPP-based
software defined radios. Proc. of the Military Communications Conference.
San Diego, CA. 2013. pp. 1592–1596.
[136] Xilinx. Xilinx ISim User Guide. USA: Product specification. 2011.
[137] Xilinx. Lower Verification Times by up to 50 % Debug often consumes. USA:
Product specification. 2008.
[138] D. Andrews and B. J. Agron. Modeling Abstractions for Next Generation
Reconfigurable Computing. Proc. of the International Conf. Reconfigurable
Systems and Algorithms. Nevada, USA. 2008. pp. 49-54.
[139] Pimentel, J. C. G. Implementation of Simulation Algorithms in FPGA for Real
Time Simulation of Electrical Networks with Power Electronics Devices.
Proc. of the IEEE International Conference on Reconfigurable Computing
and FPGA's. San Luis Potosi. 2006. pp. 1-8.
[142] Bakhteri,R. SoC-based Design of Arrhythmia Detector. Proc. of the 2nd
International Conference on Electronic Design. Penang, Malaysia. 2014. pp.
42-46.
[143] Yamada, H., Sato, Y., Ooshima, N., Hirai, H., Suzuki,T., and Minami, S..
Heterogeneous system integration pseudo-SoC technology for Smart-health-
care Intelligent Life Monitor Engine & Eco-system (Silmee). Proc. of the
Electron. Components Technol. Conf., 2014. pp. 1729–1734.
PTTAPERP
USTAKAAN TUNKU T
UN AMINAH
