DSP II – Final presentation 16/12/2008 1Hans De Clercq & Rogier Corthout.

Hans De Clercq & Rogier Corthout 1

Respiratory monitoring

DSP II – Final presentation

16/12/2008

Hans De Clercq & Rogier Corthout 216/12/2008

Some background…


Project integrated in master thesis:◦ “Textile-integrated data-acquisition system”◦ Prof. Dr. Ir. R. Puers◦ Optional course in Biomedical technology

Application for monitoring breathing disorders (e.g. SIDS) during sleep for babies

Accelerometer-based design measuring movements during breathing

Started from scratch shaped our own DSP-project…

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Some background…


Non-uniform chest/abdomen expansion measurement variation of inclination using accelerometers placed sideways on the chest/abdomen

XY-plane modulus & angle

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Some background…

Respiration

g


Raw signal vs gold standard

Accelerometer (angle) Spirometer

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Time (samples @ 20 Hz)

Time (s)


Signal conditioning•Noise cancellation from low-cost accelerometers•Offset cancellation

Adaptive filtering •Extraction dominant breathing frequency using adaptive band-pass filter

Information processing•Extraction RMS•Phase shift with reference signal

Decision making •Fuzzy clustering: distinction heavy/quiet breathing, coughing, apnoea…

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Objectives project DSP II


Block scheme

Offset & noise

cancellation

Nishimura or

N-points FFT

Adaptive BPF

Extraction dominant frequency

RMS

Accelerometer signal

Spirometer signal

Phase shift

Decision making (fuzzy logic)

Breathing rate

Breathing pattern

Error rate


Noise cancellation from low-cost accelerometers outside useful breathing BW◦ Noise limits accelerometer resolution:

1000μg/sqrt(Hz)◦ Analog filtering (simple RC) anti-aliasing: sample

frequency ADC ~ 10 Hz Offset cancellation

◦ Simple, but steep high-pass IIR-filtering (cut-off ~ 0.01 Hz)

◦ E.g. third order Chebychev Normalization with reference signal

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Signal conditioning

Signal conditioning

•Noise cancellation from low-cost accelerometers•Offset cancellation

Adaptive filtering

•Extraction dominant breathing frequency using adaptive band-pass filter

Information processing

•Peak and amplitude detection•Phase shift with gold standard

Decision making

•Heavy/quiet breathing, coughing, talking and undefined signal


Signal conditioning


Adaptive filtering




Decision making


Objectives:◦ Cancel the noise inside the useful breathing BW,

obtaining only the “sine wave”-like signal◦ Obtain the dominant frequency as a parameter

for the pattern detector

Comparison with gold standard…

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Adaptive filtering


Two methods explored:◦ Nishimura method◦ STFT-method

Algorithms tested on 3 different signals:1. Sine wave with Gaussian noise

4 different frequencies/amplitudes SNR < 2dB

2. Spirometer measurement (gold standard)3. Accelerometer measurement (test signal)4. … and those two combined …

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Adaptive filtering


Concept: adaptive band-pass filter (IIR!) Filter is tuned in real-time to maximize the

output of the bandpass filter:◦ Basically, input is a “sine wave” with a variable

frequency covered in noise…◦ Bandpass filtering for maximum output

= looking for the frequency band with maximum power!

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Nishimura method


Input signal

Frequency (Hertz)

Time (s)


2nd order IIR-filter:

α0 determines the selectivity of the filter…

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Nishimura method [2]

20

1 21 0 0

1 1( ) .

2 1 ( )(1 )B

zH z

k z z

Increasing α0


2nd order IIR-filter:

α0 determines the selectivity of the filter α1 determines the center frequency of the

pass-band:

α1 is iteratively tuned in real-time to maximize the output of the bandpass filter…

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10 1

1cos ( )k

T

20

1 21 0 0

1 1( ) .

2 1 ( )(1 )B

zH z

k z z


Iterative updating scheme for α1:◦ Gradient algorithm towards maximum output

power

◦ μ determines the convergence speed, and is heavily related with stability…

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2

1

( )2 ( ) ( )

( )

y ky k k

k

xμ+

y(k)

Δ

u(k)HB(z) G(z)

1 0

0 0

( ) ( )(1 ) ( 1)

( 2) (1 ) ( 1)

k k k

k y k

1 1( 1) ( ) ( ) ( )k k y k k


Strengths Weaknesses

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Nishimura method: results


Update filter for every new input value Computational efficiency real-time

implementation possible

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xμ+

y(k)

Δ

u(k)HB(z) G(z)

1 0

0 0

( ) ( )(1 ) ( 1)

( 2) (1 ) ( 1)

k k k

k y k

1 1( 1) ( ) ( ) ( )k k y k k


Strengths Weaknesses

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Limited convergence speed if varying frequency…

E.g. on sine wave signal with varying frequency and amplitude…

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Time (s) Time (s)

Dete

cted

dom

inan

t fr

eq

uen

cy (

Hz)

Am

plit

ud

e in

pu

t si

gn

al


Strengths WeaknessesLimited convergence

speed if varying frequency

Stability problems of IIR-filter

Update filter for every new input value

Computational efficiency real-time

implementation

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Nishimura method: conclusion Works “fine” on artificial sine wave signal… Biomedical signals aren’t deterministic at all

limited convergence speed = bottleneck in detecting the dominant frequency

E.g. on simple spirometer signal…

Time (s) Time (s)

Dete

cted

dom

inan

t fr

eq

uen

cy (

Hz)

Am

plit

ud

e in

pu

t si

gn

al

Estimation of real

frequency

Unstable working regime…


After paper Hung & Bonnet… Divide signal in 51.2s (1024 samples)

segments with 1/6th overlap between windows for edge continuity

Assumption: continuity over time window Calculate spectrum and detect maximum

freq. of accelerometer signal f0 ∈ 0.1-1Hz Filter the signal in pass-band:

using a 4th order Butterworth filter…

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STFT-method

0 0max(0.1 , 0.4 ) 0.4Hz f Hz f f Hz


Strengths WeaknessesAccuracy: Heisenberg

principle

Not in real-time

No convergence problems

No stability problems

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STFT-method [2]


STFT-method [3] E.g. on spirometer signal (trivial on sine

wave)

frequency time

less smearing out of low breathing

frequencies


E.g. applied on spirometer signal… Limited frequency resolution But still more reliable than Nishimura for

long term monitoring…

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STFT-method [4]

Time (s) Time (s)

Dete

cted

dom

inan

t fr

eq

uen

cy (

Hz)

Am

plit

ud

e si

gn

al

Filtered signal Detected frequency


Comparison using accelerometers

Original signal

Filtered (STFT method)

Filtered (Nishimura)

Similar results:• STFT more reliable and

intuitive• Nishimura hard real-time

potential… STFT seems to be the

wisest choice for long-term applications!

Time (s) Time (s)

Dete

cted

dom

inan

t fr

eq

uen

cy (

Hz)

Am

plit

ud

e si

gn

al

Time (s) Time (s)

Dete

cted

dom

inan

t fr

eq

uen

cy (

Hz)

Am

plit

ud

e si

gn

al

Am

plit

ud

e si

gn

al


Comparison of performance on spiro & accelero

Accelerometer signal Spirometer signal

Filtered signal using STFT

Filtered signal using Nishimura

Detected frequency using STFT

Detected frequency using Nishimura

Consistent detection on both signals

Inconsistent detection due to unstable

behaviour…


Dominant frequency (supra) RMS-value of BP-filtered signals Phase shift with gold standard

◦ Normally consistently small during quiet breathing [GOLLEE]

◦ Exception: transient/fast movement, e.g. forced expiration (coughing)

◦ Implemented, but not yet used for this application…

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Signal conditioning


Adaptive filtering




Decision making


http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4352768


Signal conditioning


Adaptive filtering




Decision making


Determination of breathing pattern from processed information

Clustering of sampled values for all parameters using fuzzy techniques Advantage: clustering techniques can reveal

structures in data without relying on assumptions common to conventional statistical methods, such as the underlying statistical distribution

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Decision making


Data set to be clustered:

Every time sample has its membership function μik:

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Decision making [2]

11 1

1

N

n nN

z z

Z

z z

Time samples k=1…N

Clu

ster

vari

able

s 1…

n

1

1

0,1 , 1 , 1

1 , 1

0 , 1

ik

c

iki

N

iki

i c k N

k N

N i c


Objective is to minimize the fuzzy c-means functional:

Implementation of Gustaf-Kesselson algorithm in Matlab◦ Complex iterative algebraic problem, further

mathematical details omitted…◦ Only input parameter: expected number of clusters c

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Decision making [3]

2

1 1

( ) , 2i

c Nm

ik ikAi k

J D m

Membership function of sample k to cluster i

Distance of sample k to center of cluster i


Output of algorithm:◦ Partition matrix with all

membership functions U

◦ Cluster prototype matrix V with cluster centers

◦ Cluster covariance matrix F

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Decision making [4]11 1

1

N

c cN

U

11 1

1

n

c cn

v v

V

v v

11 1

1

n

c cn

f f

F

f f

Time samples k=1…N

Clu

ster

num

ber

1…

c

Cluster variables k=1…n

Clu

ster

num

ber

1…

c

Cluster variables k=1…n

Clu

ster

num

ber

1…

c


Detection mean amplitude or average power per breathing pattern

Application on artificial test signals obtain low error rates during clustering:◦ ε < 15% using Nishimura◦ ε < 5% using STFT-BPF!

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Results and discussion


Results and discussion [2]

Dominant frequency (STFT)

RM

S S

TFT

-BPF o

utp

ut

Long-term measurement using only accelerometers…

Quiet breathin

g

Heavy breathin

g

No breathin

g

boxplot RMS

boxplot dom. freq.

point cloud

Cluster number

Clu

ster

num

ber


Gustaf-Kesselson starts with random cluster centers use statistical information to initialize clustering more reliably

Expand frequency detection & fuzzy clustering to other signals (ECG, oximetry,…) in order to:◦ Obtain redundant measurements increase

reliability◦ Examine the correlation between those signals

during apnoea

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Future work & potential…


Respiratory monitoring

DSP II – Intermediate presentation

16/12/2008

DSP II – Final presentation 16/12/2008 1Hans De Clercq & Rogier Corthout.

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Transcript of DSP II – Final presentation 16/12/2008 1Hans De Clercq & Rogier Corthout.