Analysis of RS-Segment to Evaluate the Eect of VentricularDepolarization during Ischemia

Akash Kumar Bhoi1,, Karma Sonam Sherpa2 and Sushant Konar1

1Department AE&I Engg., 2Department E&E Engg.,Sikkim Manipal Institute of Technology (SMIT), Majhitar.

e-mail: 1akash730@gmail.com

Abstract. The QRS changes during ischemia have historically been more dicult to parameterizeand have not come into clinical practice. This paper presented a new approach to analyze ischemiaby time parameter extraction of RS-Segment of the QRS complex. The proposed methodologymainly focused on two prominent areas; rst: detection of R and S points via Fast Fourier Transform(FFT) based windowing & thresholding techniques with a sliding edge method. Second: calculatingthe RS-Duration. The performances of the detection methods are validated and RS-Duration isevaluated with the Fantasia database (Fantasia) for 20 healthy subjects & Long-Term ST Database(LTSTDB) for 80 ischemic patients. The RS-Segment detection sensitivity (Se) and specicity (Sp)are calculated 100% for Fantasia Database, whereas sensitivity (Se) is 91.6% and specicity (Sp) is974% for LTSTDB.

Keywords: Ischemia, RS-Segment, Fast fourier transform, Windowing, thresholding, Sliding edgemethod, FANTASIA, LTSTDB.

1. Introduction

Myocardial ischemia causes changes in the STT wave, but unlike a full thickness myocardial infarctionhas no direct eects on the QRS complex [4]. Computer-generated X-Y plots were used to examine thecorrelations between the magnitude of S-T depression and the R wave and total RS amplitudes [14].In [6], ischemic changes in the electrocardiogram (ECG) may precede angina pain. In [8], wideningof the QRS complex or amplitude change and QRS slope information help in myocardial ischemiadetection.

Recent studies have suggested that a decrease in high-frequency content (150250Hz) of the QRScomplex is a better marker of ischemia than the traditional ST index [1012]. Early animal studiesdemonstrated changes in QRS morphology due to slowing of intra-myocardial conduction during ischemia[911]. In [13], RMS voltage reduction of the high-frequency QRS components (HFQRS) presents largeinter individual variation, making this index incompetent for separation of subjects with and withoutcoronary artery disease (CAD) & MI [13,1]. The R-waves of ECG are detected using slope detectiontechnique and proper thresholding [2]. Calculation of Instantaneous Heart Rate (IHR) described in [3].

Corresponding author.

ICC-2014 Editors: K. R. Venugopal and A. C. Ramachandra pp. 105111. 105

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Figure 1. ECG inection points.

In a number of epidemiological studies involving ventricular repolarization abnormalities in the electro-cardiogram (T wave morphology and QT interval prolongation) related with Sudden Cardiac Death [15]and of cardiovascular death, [16] probably because they could be markers of ventricular hypertrophy, leftventricular dysfunction or myocardial ischemia [17]. In [7], authors proposed HamiltonTompkins andHilbert transform-based methods for QRS detection and modied threshold method. Evaluation of QTintervals for acute myocardial ischemia analysis proposed in [18]. QRS morphology is used for the pur-pose of cardiac arrhythmias diagnosis, conduction abnormalities, ventricular hypertrophy, myocardialinfarction, electrolyte derangements, etc. [19]. Changes during depolarization phase (the QRS complex)of the ECG also add information regarding ischemia [20].

The Proposed methodology is applied to analyze these changes and establish a relationship betweenthe eects of RS-Segment (Figure 1) during myocardial ischemia. The detection performance is validatedwith Fantasia & LTSTDB databases.

1.1 The QRS Complex Features

The QRS complex is an important part of the ECG signal carrying a number of clinically signicantparameters of cardiac arrhythmia. The duration of QRS is one of the main characteristics of this complexand can be used in analysis and classication of the ECG signal. This parameter is dened as the timeit takes for depolarization of the ventricles [5].

Based on the medical denition [21], QRS interval is the duration between the onset and the oset ofthe QRS complex. Its normal duration is 0.040.11s (i.e., 1540 sampling points at a sampling frequencyof 360Hz). Tape #103 in the MITBIH arrhythmia database is an example [22], taken for this study andthe duration time is 20 sampling points, where the distance from point Q to point R is 11 (255266)sampling points, and point R to point S is 9 (266275) sampling points, respectively. Previously discussedmethods suggests, the major ischemia detection being carried out by analyzing ST-segment and T-wavewhich is the continuation of RS-Segment. The proposed methodology is focused on formulating cohesivelink between ischemia and RS-Duration. The performance measurement and evaluation of the detectiontechniques are veried with the LTSTDB and FANTASIA databases.

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Analysis of RS-Segment to Evaluate the Eect of Ventricular Depolarization during Ischemia

2. Methodology

2.1 ECG Database

The Long-Term ST Database contains 86 ECG recordings of 80 human subjects (s20011m, s20021m . . .s30801m) [23], selected to present dierent subject data for ST-Segment changes. The proposedmethodologies have been tested over 2 rows (signals) and 2500 columns (samples/signal) with theduration of 10 sec having sampling frequency: 250Hz & sampling interval: 0.004 sec [24]. Fantasiarecords f1y01, f1y02 . . . f1y10 and f2y01, f2y02 . . . f2y10) were obtained from the young cohort, andrecords f1o01, f1o02 . . . f1o10 and f2o01, f2o02 . . . f2o10) were obtained from the elderly cohort. Eachgroup of subjects includes equal numbers of men and women [25]. These two dierent sets of databases(i.e. ischemic & healthy patients data) are collected for the evaluation of the proposed methodology.

2.2 Fast Fourier Transform (FFT)

Fourier transform is an integral of the form [26]:

F (u) =

f(x)ei2uxdx (1)

ei = cos() + i sin() (2)For sampled function continuous transform (1) turns into discrete form [26]:

Fn =N1k=0

fkei 2N kn (3)

2.3 Inverse Fourier Transform

Expression for inverse Fourier transform is

f(x) =

F (u)ei2uxdu (4)

and its discrete counterpart is

fk =1N

N1n=0

Fnei 2N kn (5)

2.4 RS-Segment Detection

Following steps are being implemented on the ECG signals to Detect R and S points.

FFT based windowing & thresholding techniques

Step-1: FFTRemoval of low-frequencies component

Step-2: IFFTRestoration of ECG signal

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Figure 2. RS-Segment detection of LTSTDB patient data s20071m.

Figure 3. RS-Segment detection of FANTASIA patient data f2y09m.

Figure 4. Localization of RS-Segment for single ECG waveform (of FANTASIA data f2y09m) for bettervisualization.

Step-3: Windowed lter (default size)Localization of maxima (only maximum in his window and ignores all other values)

Step-4: Threshold FilterRemove small values and preserve signicant ones

Step-5: Repeat Step-3 with adjusting size of the windowed lter to improve ltering performance.

Step-6: R-peaks detected

Sliding edge method

Step-7: Find the sample value of R-peaks(Let x be the sample value of detected R-peaks)

Step-8: Sliding edge method to detect S pointsCompare x & x + 1 and detect the value of S if, x < x + 1

Step-9: Finally, RS-Segment detected (shown in Figure 2, 3 and 4).

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Analysis of RS-Segment to Evaluate the Eect of Ventricular Depolarization during Ischemia

Table 1. RS-Duration of all subjects from LTSTDB and FANTASIA databases.

2.5 RS-Duration Calculation

The LTSTDB signals are having length of 10 sec (2500 samples). RS-Duration is calculated for theselected initial waveform (i.e. 500 samples or 2 sec data) of the full length signal. The length of thedetected RS-Segment is multiplied with sampling interval (i.e. 0.004 sec) to obtain the RS duration.Table 1 shows the calculated RS duration of each ECG signals of Fantasia and LTSTDB.

3. Results Analysis

The performance of the methodologies is evaluated by the sensitivity (Se) and the specicity (Sp). The Seand Sp are normally computed by:

Se = 1 FNTP + FN

=TP

TP + FN(6)

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Sp = 1 FPTP + FP

=TP

TP + FP(7)

False positive (FP) indicates that the algorithm detects a beat when no beat is present; whereas, a Falsenegative (FN) indicates that the algorithm failed to detect a real beat. TP (true positive) stands for thebeat, properly detected [19]. The sensitivity (Se) = 100% and specicity (Sp) = 100% for the FantasiaDatabase and the LTSTDB results are Se = 91.6% and Sp = 97.4%. The FP and FN are highlighted onthe Table 1. The idea of calculating RS-Duration for both the Fantasia and LTSTDB is to project theeect on healthy and ischemic ECG signal during ventricular depolarization. The mean RS-Duration ofFantasia and LTSTDB are 0.0323 sec & 0.0430 sec, respectively. The dierence in the RS-Durations ofnormal and ischemic signals shows the abnormal changes in RS-Duration during ischemia. The variationin RS-Duration can be observed in LTSTDB (Table 1) where, minimum RS-Duration is found to be0.0200 sec (s20131m, ML2 lead) and maximum is 0.1120 sec (s30671m, V6 lead).

4. Conclusion

The previously proposed scheme uses various stages, including pre-processing, conditioning and postprocessing. The implemented methodologies have overcome such pre-processing stages. New methodo-logies have been discussed for parameterization of RS-Segment with optimum results. Se and Sp are foundto be 100% for healthy signals (i.e. FANTASIA) and 91.6% & 97.4% for patients with ischemic conditions(i.e. LTSTDB) respectively. The mean RS-Duration dierence between FANTASIA and LTSTDB is0.0107 sec, which shows widening of RS-Segment during ischemia. This analysis will help the academiccommunity and researchers to explore the further ndings in the ischemic heart diseases.

References

[1] M. Ringborn et al., The absence of high-frequency QRS changes in the presence of standard electrocardio-graphic QRS changes of old myocardial infarction, Amer. Heart J., vol. 141, no. 4, pp. 573579, (2001).

[2] M. A. Khayer et al., ECG peak detection using wavelet transform, 3rd International Conference onElectrical & Computer Engineering (ICECE) 2004, 2830, Dhaka, Bangladesh, December (2004).

[3] I. Daubechies, Ten lectures on wavelets, Society for Ind. and App. Math. Philadelphia, Pennsylvania.ISBN 0 89871-274-2, (1992).

[4] Kevin Channer et al., ABC of clinical electrocardiography Myocardial ischemia, BMJ, vol. 324, April(2002).

[5] H. Gholam Hosseini et al., Ecient features for Ann-based ECG classiers, IEEE Bio., (1999).[6] E. Pueyo et al., Time course of ECG Depolarization and Repolarization Changes during Ischemia in PTCA

Recordings, Methods Inf Med., vol. 43, pp. 436, (2004).[7] Natalia M. Arzeno et al., Analysis of First-Derivative Based QRS Detection Algorithms, IEEE Trans-

actions on Biomedical Engineering, vol. 55, no. 2, February (2008).[8] Esther Pueyo et al., QRS Slopes for Detection and Characterization of Myocardial Ischemia, IEEE Trans-

actions on Biomedical Engineering, vol. 55, no. 2, February (2008).[9] R. P. Holland and H. Brooks, The QRS complex during myocardial ischemia: An experimental analysis in

the porcine heart, J. Clin. Invest., vol. 57, pp. 541550, (1976).[10] J. Pettersson et al., Changes in high-frequency QRS components are more sensitive than ST-segment

deviation for detecting acute coronary artery occlusion, J. Amer. Coll. Cardiol., vol. 36, pp. 18271834,(2000).

[11] R. L. Hamlin et al., QRS alterations immediately following production of left ventricular freewall ischemiain dogs, 4m. J. Physiol., vol. 215, pp. 10321040, (1968).

[12] A. Beker, et al., Analysis of high frequency QRS potential during exercise testing in patients with coronaryartery disease and in healthy subjects, Pacing Clin. Electrophysiol., vol. 19, no. 12, pp. 20402050, (1996).

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[13] S. Abboud, et al., High frequency electrocardiography using an advanced method of signal averaging fornon-invasive detection of coronary artery disease in patients with normal conventional electrocardiogram,J. Electrocardiol., vol. 19, no. 4, pp. 371380, (1986).

[14] M. C. Gerson et al., Relation of exercise-induced physiologic S-T segment depression to R wave amplitudein normal subjects, Am J. Cardiol., (1980).

[15] S. M. Straus, J. A. Kors, M. L. de Bruin, C. S. van der Hooft, A. Hofman, J. Heeringa et al., ProlongedQTc interval and risk of sudden cardiac death in a population of older adults, J. Am Coll. Cardiol., vol. 47,pp. 3627, (2006).

[16] D. De Bacquer, G. de Backer, M. Kornitzer and H. Blackburn, Prognostic value of ECG ndings for total,cardiovascular disease, and coronary heart disease death in men and women, Heart, vol. 80, pp. 5707,(1998).

[17] M. H. Lehmann and F. Morady, QT interval: metric for cardiac prognosis, Am. J. Med., vol. 115,pp. 7324, (2003).

[18] Javier Jimenez-Candil et al., QT Interval and Acute Myocardial Ischemia: Past Promises, New Evidences,Rev. Esp. Cardiol., vol. 61(6), pp. 5613, (2008).

[19] Zine-Eddine Hadj Slimane et al., QRS complex detection using Empirical Mode Decomposition, Else-vier Inc. All rights reserved, (2009).

[20] D. Romero et al., A Vectorial Approach for Evaluation of Depolarization Changes during Acute MyocardialIschemia, Computing in Cardiology, vol. 37, pp. 265268, (2010).

[21] R. M. Rangayyan, Biomedical Signal Analysis: A Case-study Approach, WileyInterscience, New York,pp. 1828, (2001).

[22] Yun-Chi Yeh et al., QRS complexes detection for ECG signal: The Dierence Operation Method,Computer methods and programs in biomedicine 91 pp. 245254, Elsevier Ireland Ltd., All rights reserved(2008).

[23] A. L. Goldberger et al., PhysioBank, PhysioToolkit, and PhysioNet: Components of a New ResearchResource for Complex Physiologic Signals, Circulation 101(23): e215-e220 [Circulation Electronic Pages;http://circ.ahajournals.org/cgi/content/full/101/23/e215], June (2000).

[24] Franc Jager et al., Long-term ST database: a reference for the development and evaluation of automatedischaemia detectors and for the study of the dynamics of myocardial ischaemia, Medical & BiologicalEngineering & Computing, vol. 41(2), pp. 172183, (2003).

[25] N. Iyengar et al., Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics,Am. J. Physiol., vol. 271, pp. 10781084, (1996).

[26] Mohamed Elgendi, Fast QRS Detection with an Optimized Knowledge-Based Method: Evaluation on 11Standard ECG Databases, PLoS One. 2013; 8(9): e73557. doi: 10.1371/journal.pone.0073557, Sep. (2013).

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