An Automated Approach to Beat = by= Bea t QT- I n t erval Analysis

Scale

2’

2‘

A New Concept for Identifying Precursors to Sudden Cardiac Death

Lower 3 dB Freq. (Hz) Upper 3 dB Freq. (Hz)

31.5 80.0

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ardiovascular diseases are a substantial C cause of death in the adult population. These diseases have engaged researchers to develop new tools to diagnose the onset of these diseases, which could lead to mortality [ I , 21. Surface recording of car- diac parameters is a noninvasive tech- nique for clinicians to study the

. pathological state of the heart. Some of these parameters carry important infor- mation and have special significance in research, as they are good indicators of diseases leading to heart attacks and simi- lar cardiac problems. The QT interval is one such parameter that has received the maximum attention of researchers.

Dilated cardiomyopathy (DCM) is a highly prevalent and lethal disease in the United States. When accompanied by congestive symptoms, DCM has been as- sociated with an annual mortality rate of 25% to 40%, regardless of origin. Half of these deaths occur suddenly and unex- pectedly, as has been reported by Berger, e t a l . , in his recent paper [3] on beat-by-beat QT-interval variability. Ac- cording to his hypothesis, DCM leads to abnormal beat-to-beat changes in ventric- ular repolarization. He also indicates that there is growing evidence that myocardial repolarization is altered in the setting of heart failure. While numerous studies have examined the physiological and prognostic value of heart-rate variability, there is a paucity of literature regarding beat-to-beat QT-interval variability. This is due, in part, to the technical difficulties in developing robust automated schemes for QT-interval determination. In assess- ing QT-interval variability, determination of absolute QT duration is relatively un- important, but the method must be sensi- tive to subtle changes in QT interval from one beat to the next as well as relatively insensitive to signal noise.

Several techniques have been reported for QT-interval measurement [2, 4, 51. Problems in detecting the T-wave are also

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J.S. Sahambi’, S.N. Tandon’, R.K.P. Bhatt2 ’Center for Biomedical Engineering, ’Center for Electrical Engineering, lndion Institute of Technolgy, Delhi

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cited in the literature, due to low-fre- quency components such as baseline wan- der [4, 51. Earlier research work focused on the QT interval as an indicator of sud- den death [6-91. The QT interval generally has been taken as total ventricular systole (ventricular activation), and this includes the depolarization and repolarization of the ventricles. In the context of Berger’s hypothesis, that DCM leads to variability in repolarization, it was necessary to seg- ment the QT interval into depolarization (QRS width) and repolarization (ST-T and T-widths) phases [IO].

The present work attempts to resolve some of the problems encountered in QT in- terval analysis and to describe a new algo- rithm that uses wavelets to analyze the QT interval on a beat-by-beat basis. The QT in- terval i s further segmented into its contrib- uting factors to find the variability in the repolarization phase. The technique and the algorithm have been tested on control and disease data from the standard MIT ECG li- brary to prove the reproducibility and accu- racy of the system. The technique has been implemented on a PC DSP add-on card to do the analysis on a beat-by-beat basis.

Wavelets Wavelet transformation is a linear op-

eration that decomposes a signal into

components that appear at different scales (or resolutions) [ I 1-14]. The wavelet transform of f ( t ) , with ~ ( t ) as the mother wavelet, is defined as the inner product of f ( t ) with V,,(@

wheie

and a is the scale parameter and T is the shift parameter. Here, ~ ( t ) is the mother wavelet, which satisfies the admissibility conditions [14]. The mother wavelet and its scaled versions act as bandpass filters on the signal. The wavelet we used is the first derivative of a smoothing (Gaussian) function [15]. This wavelet is infinitely differentiable and thus has the ability to detect subtle changes.

Methodology The analog ECG data are digitized at

the rate of 250 samples per second [16] with a 16-bit bipolar analog-to-digital converter having a dynamic range of k10 V. The gain of the differential amplifier is lo3. The resolution of the system is 0.152 pV. The digitized data are analyzed by the TMS320C25-based DSP add-on card to a host IBM PC. The PC acquires the results of analysis for display and storage. The detection and localization of the QT inter- val requires the detection of onsets and offsets of the QRS complex, the T-wave, and the J-point. This is done after reliable detection of the QRS complex [ 151.

.- ~ .~ - 1 Table 1. Pass Bands of Wavelet Filters at Four Scales - .~ --

Moy/June 2000 IEEE ENGINEERING I N MEOlClNE AND BIOLOGY 0739-51 75/00/$10 00020001EEE 97

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1 shows the pass bands of the wavelet fil- ters at four scales.

The onset and offset of the QRS are de- tected by using scale 2*, because the ECG around these turning points is composed of high frequencies. The zero crossing of the Wf(2’, z) function before the maxima (the one corresponding to the QRS com- plex) corresponds to the onset of the QRS. Similarly, the zero crossing of Wf(2*,z) after the maxima (corresponding to the QRS complex) corresponds to the offset of the QRS point. Earlier research has shown that with careful selection of mother wavelet and analysis scale, accu- rate results can be obtained even in the presence of noise [ 11.

QRS Onsets and Offsets The detection of onsets and offset of

QRS complex and T-wave are based on the modulus maxima (maximum absolute value) and zero crossings of the wavelet transforms at the characteristic scales [15]. The wavelet transforms are com- puted only for the scales of interest. Table

Wavelet Approach Versus the Differentiater Approach

for Onset and Offset Detection Laguna, et al. [2], has used the deriva-

tive of the ECG to locate the onset and off- set of the QRS and the offset of the T-wave. This technique, however, could amplify the noise and introduce eqors. The onset and offset of the QRS complex are high-frequency phenomena, whereas the T-wave is a low-frequency phenome- non. Therefore, the onset and offset of the QRS complex are to be detected on scale

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QRS Width 1 ‘’ 0 20 40 60 80 100 120 140 160

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1. Definition of QT interval and its segments in ECG.

98 IEEE ENGINEERING IN MEDICINE AND BIOLOGY

2’ or 22, and the T-wave has to be analyzed on scale 23. Using scale 2’ reduces the ef- fect of power-line interference. There- fore, detection by the wavelet approach is more accurate.

Detection of S-Point and J-Point For the ST segment, the S-point and

the J-point are detected by using the scale 2’ because the ECG around these points is composed of high- frequencies. The zero crossing of Wf(2’,z) after the maxima (corresponding to the QRS complex) cor- responds to the S-point. The J-point is de- fined as the first inflection after the S-point, and in some cases it can be the same as the S-point. For the J-point, first a peak is detected in scale 2’ after the above-mentioned zero crossing. To detect the J-point, a peak (of opposite polarity than the modulus maxima before the S-point) is searched for in the range of 20 msec after the S-point. After this, a point of maximum slope is searched for. The J-point is then defined as the point where the slope of Wf(2,,2) becomes less than 50% of the maximum slope after the point at which the maximum slope occurs. In case the point of inflection is not found af- ter the S-point in the given interval, then the J-point is taken as the S-point.

T Onset and Offset The T-wave corresponds to a pair of

modulus maxima of the wavelet trans- form at scales 23 located after the end of QRS complex, with a zero crossing in be- tween. The zero crossing between the modulus maxima gives the position of the T-peak (this is true for both positive and negative T-waves). The modulus maxima correspond to the maximum slopes be- tween the onset and the T-peak, and be- tween the T-peak and the offset. The search for the onset of the T-wave is car- ried out between the first modulus max- ima corresponding to the T-wave (maximum slope between the onset and the T-peak) and the QRS offset.

Let z,, and z,, be the times corre- sponding to the first and second modulus maxima, respectively. The first modulus maxima i s denoted by T P e a k l = Wf(23,z,,) and the second by TPeak2 = ~ f ( 2 ~ , z , , ) . Starting from z,,, a point is searched backwards where Wf( 23, 7) satis- fies any one of the following conditions:

The value of Wf(23, z) becomes less then or equal to the threshold THPl = T P l k p l . Here kpl is a constant with a value of 4.

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Moy/June 2000

The slope of Wf(23, z) changes sign (without reaching THPl) There is major change in the slope of

This point is marked as the onset of the T-wave.

For the T-offset, a search is made from the point T~~ onwards, and a point is searched where the Wf(23, 7) satisfies any one of the following conditions:

The value of k~f(2~,7) becomes less then or equal to the threshold, THP2 = TP2kp2. Here, kp2 is a constant with a value of 4. The slope of Wf(Z3,~) changes sign (without reaching THP2). There is major change in the slope of

This is marked as the offset of the T-wave. In this approach, the thresholds, THPl and THP2, are dependent on TPeakl and TPeak2. This takes into account sepa- rately the rate of decrease of slope for on- set and offset detection, increasing the accuracy as compared to other absolute threshold methods in the presence of baseline wander.

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101

116

119

123

124

22 1

ST Segment Conventional methods for ST-segment

analysis use a) J+x, b) R+x, and c) win- dowed approaches. J+x and R+x are com- monly used by researchers and in commercial ECG monitors. The ST seg- ment is defined as a part of the ECG located between points J and J+x msec. Literature reports the use of x = 80 msec for heart rates less than 120 bpm, and a value of 60 msec for a heart rate greater than 120 bpm. The Philips Viagraph monitor,makes this typical measurement at J+80 msec. The Marquette monitor allows the user to select the ST point at 0,40,60, or 80 msec after the J-point. The R+x method uses the R-peak as the reference point. Due to the empirical nature of the approach, it intro- duces errors at high heart rates. In the win- dow approach, the various algorithms are used to determine the ST-segment end point in a window after the J-point [ 161.

The presence of noise can severely af- fect the performance of the above ap- proaches. Additionally, the above formulae are derived from physiological factors and can also be influenced by noise. At higher heartrates (i.e.,inthethirdorfourthstageof a treadmill test), the value ofx must be made smaller to give correct results.

To overcome these limitations, the technique developed by the authors for ST-segment analysis uses wavelet trans-

41 9.45 451.91 97.37 97.52 224.54

372.07 426.01 77.68 66.17 228.21

435.68 453.83 67.06 130.93 237.68

457.66 41 7.54 75.18 150.83 231.64

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forms [ 171. In this technique, the fiducial points of the ECG-signal are determined with greater accuracy than with the exist- ing methods [18]. Here, the ST segment is defined as the segment between the J-point and the onset of the T-wave. The end point of the ST segment (T-onset) is the inflection point between the J-point and the T-peak. This is the T-onset. How- ever, if this inflection point is not present, then the standard empirical formula is used for the detection of the T-onset. The ST segment is the interval between the J-point and the T-onset. This approach works well for different morphologies (ST values) and heart rates. In this tech- nique, the ST-T junction is found by de- termining the point of inflection with the wavelet transforms. Therefore, no spe- cial modification is required for high heart rates.

QT Interval Once the QRS onset and the T-wave

offset have been detected, the QT interval is defined as the time interval between these two points; i.e.,

QT = zwfset - (3) To take into account the variability of the R-R intervals, Bazett’s formula is applied to obtain QTc (QT corrected) [19]:

(4) where RR is the R-R interval of the previ- ous beat.

The performance of these techniques has been tested. It was found that with a maximum noise level of 1 mV (peak to peak) in a 2 mV ECG signal, the maxi- mum errors in the widths of QRS-, P-, and T-waves were 6.2576, 4.796, and 5%, re- spectively [ 11.

In order to establish the proposed tech- nique, ECG data for control groups as

well as for diseased groups were taken from the MIT standard library.

Results and Discussion Earlier research work focused on the

total QT interval as an indicator of sudden cardiac death. With accurate timing inter- val characterization using wavelets, the QT interval has been further divided into its segments-the QRS, the T-wave, and the ST segment, to correlate the variations in these segments with cardiac disease, and to find the variability in the repolar- ization phase.

A large number of data files were ana- lyzed from the standard MIT ECG data- base QT interval. Results of six data files (nos. 101, 116,119, 123,124, and221)are shown in this article. Recent studies [3] have shown that in pathological cases, the Q T interval varied widely f rom

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Table 2. Average Values of QT interval and Its Segments of MIT Data Files (All Values Are in Millisec)

1 --‘I - y- I T-Width I QT I QTc I QRSWidth

Number I

Moy/Jsne 2000 IEEE ENGINEERING IN MEDICINE AND BIOLOGY 99

ing toward his Ph.D. degree. His current research interests include digital signal processing, wavelets, biosignal process- ing, and microprocessor applications.

101

116

119

123

124

14.90 18.42 10.99 16.1 1 13.93

8.74 10.26 3.68 5.78 7.54

9.39 10.74 3.39 7.19 7.29

10.42 13.35 4.1 1 8.29 7.81

11.22 10.55 6.24 10.41 6.60

beat-to-beat despite the fact that these pa- tients had relatively stable heart rate. On the other hand, the QT interval from beat-to-beat was relatively stable in con- trol groups, with variable heart rates.

Tables 2 and 3 show the average and standard deviation of the various compo- nents (i.e., the QRS width, ST segment, and the T-width) of the QT interval for MIT database files.

File 124 has the largest QT interval of all the files analyzed. This is because of wider QRS and ST segment. The latter in- dicates a wide gap between the end of de- polarization and the start of repolarization. However, Table 3 also shows the SD for file 124 is not the highest. This means that the QT interval and its segments are rela- tively stable.

File 221 has the largest SD for QT in- terval, ST segment, and T-width, indicat- ing large beat-to-beat variations. This file has multiform PVCs. The QRS widths in this case are relatively stable, with a value of SD = 4.3. This means that the depolar- ization phase does not vary much. The ST segment has an SD = 18.8, which is high- est of all the files considered, indicating large beat-to-beat variations in this seg- ment. The T-width also has the largest SD (20.2) of the files tested. This indicates that there are large beat-to-beat variations in the repolarization of the ventricles. In file 101, the QRS width has the largest SD, indicating large beat-to-beat varia- tions in depolarization. File 119 has the largest average T-width, meaning a wide depolarization phase that is relatively sta- ble (lower value of SD).

This discussion shows that in different pathological conditions, the segments of the QT interval vary differently. Due to the importance of QT interval variability in screening patients for DCM and sudden death, our research efforts have provided a tool for accurate QT interval measure-

ment and its contributing factors: the de- polarization and repolarization phases. In this article, no attempt was made to corre- late the variability with specific patholog- ical conditions. The variability of these QT segments may be further used for heart disease classification.

Conclusions An automated and robust algorithm for

beat-by-beat QT-interval variability has been developed using the multiresolution wavelet approach. This approach uses wavelet analysis to accurately detect and mark the fiducial points: the onset and off- set of QRS wave, T-wave, and J-point, in the ECG waveform. These fiducial points are used to measure the QT interval, which is further segmented to study the depolarization and repolarization phases. As compared to earlier methods, where noise obscures subtle changes in variabil- ity of the interval, the proposed algorithm maintains accuracy even in the presence of noise. This gives higher accuracy for variability measurements. The aspect QT-interval segmentation can be ex- plored further to correlate specific dis- eases with the variability of the segments.

J.S. Suhumbi graduated with a degree in electri- cal engineering from Guru Nanak Engi- neering College, Lud- hiana, Punjab (Punjab University), in 1989. H e obta ined his M.Tech. degree in

computer technology from I.I.T. Delhi in 1991. He worked as a faculty member of the Microprocessor Appl ica t ion Programme in the Electrical Engineering Department at I.I.T. Delhi from 1992 to 1997. Currently, he is a senior project scientist in the Center for Biomedical En- gineering at I.I.T., where he is also work-

S.N. Tundon received his B.E. and M.E. de- grees in electronics en- gineering from Roorkee University in 1965 and 1968, respectively. He obtained his Ph.D. de- gree in biomedical engi- neer ing from I.I .T.

Delhi. He worked in the biomedical engi- neering unit on a collaborative program between MIT and Harvard Medical School, from 1969-1971. He has been a faculty member of I.I.T. Delhi since 1971. Currently, he is a professor in the Center for Biomedical Engineering at I.I.T. Delhi. His current research interests are biosignal processing, medical imaging, and microprocessor applications in medi- cine.

R. K. P . Bhutt received his B.E. degree from Ravishankar Univer- sity, Raipur, in 1969. He received his M.Tech. degree f rom I.I .T. Kanpur in 1971 and a Ph.D. degree in 1981 from I.I.T. Delhi. Cur-

rently, he is an associate professor in the Electrical Engineering Department at I.I.T. Delhi. His research interests include signal & image processing, adaptive con- trol, and neural networks.

J 7

Address for Correspondence: Prof. S.N. Tandon, Center for Biomedical En- gineering, Indian Institute of Technology, Delhi, Hauz Khas, New Delhi 110016, In- dia. E-mail: sntandon@cbme.iitd.emet.in

References 1 . Sahambi JS, Tandon SN, and Bhatt R K P Quantitative analysis of errors due to power-line interference and base-line drift in detection of onsets and offsets in ECG using wavelets. Med Biol Eng & Comp 35(6): 747-75 I , Nov. 1997. 2. Laguna P, Thakor NV, Caminal JPR, and Yoon HR: New algorithm for QT interval analy- sis in 24-hour Holter ECG: Performance and ap- plications. Med Biol Eng & Comp 28: 67-73, 1990. 3. Berger RD, Kasper EK, Baughman KL, Baughman E, Calkins H, and Tomaselli F: Beat to beat QT interval variability - Novel evidence for repolarization liability in ischemia and non-ischemic dilated cardiomyopathy. Circula- tion 96(5): 1557-1565, 1997.

100 IEEE ENGINEERING IN MEDICINE AND BIOLOGY May/June 2000

4. Algara A, Brun H Le, and Zeelenberg C: An algorithm for computer measurement of QT inter- vals in the 24 hour ECG, in Computers in Cardiol- ogy, vol. 5. IEEE Computer Society Press, pp. 117-1 19, 1987. 5. Critelli GG, Marcciano F, Mazzarella M, and Miguax M: QT interval measurement of long-term ECG recordings. Application to an au- tomatic Holter analysis system, in Computers in Cardiology, IEEE Computer Society Press, pp. 481-481, 1982. 6. Anderson KP, Freedman RA, and Mason JW: Sudden cardiac death in idiopathic dilated cardiomyopathy, Ann Intern Med 107: 104-106, 1987. 7. Packer M: Sudden unexpected death in pa- tients with congestive heart failure: A second frontier. Circulation 72: 681-685, 1985. 8. Puddu PE and Bourassa MG: Prediction of sudden death from QTc interval prolongation in

patients with chronic ischemic disease. J Electrocnrdiol.19: 203-212, 1986.

gation as predictor of sudden death in patients u s i n g wavelet t ransforms for ECG with myocardial infarction. Circulation 57: Med si0l M~~ 16(1):

14. Chui, CK: Anlntroductionto Wavelets. New York Academic Press, 1992.

9. Scwhartz PJ and Wolfs: QT interval prOlOn- 15, Sahambi JS, Tandon SN, and Bhatt RKp:

characterization IEEE 1074-1077, 1987. 77-83. 1997. 10. Schaub FA: Fundamentals of Clinical Elec- trocardiography. Switzerland: J.R. Geigy, S.A. Basil Pharmaceutical Division, 1966. 11. Daubechies I: The wavelet transform - A method of time-frequency localization, in Ad- vances in Spectral Analysis (S. Haykin, Ed.) Englewood Cliffs, NJ: Prentice-Hall, 1990. 12. Mallat SG: Multiresolution frequency chan- nel decomposition of images and wavelet models. IEEE Trans.Acoust Speech Signal Proc 37( 12):

13. RioulO and Vetterli, M: Wavelet and signal processing. IEEE Signal Proc Mag pp. 14-38, Oct. 1991.

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16. Weisner SJ, Tompkins WJ, and Tompkins BM: A compact microprocessor based ST-seg- ment analyzer for operating room. IEEE Trans Biomed Eng BME-29(9): 642-648, Sept. 1982.

17. Sahambi JS, Tandon SN, and Bhatt RKP: Wavelet based ST-segment analysis. Med Biol Eng & Comp 36(5): 568-572, Sept. 1998. 18. Suzuky Y and Onu K: Personal computer system for ECG ST-segment recognition based on neural networks. Medi Biol Eng & Comp 30(8): 2-8, Jan. 1992.

19. Bazett HC: An analysis on the time relations of electrocardiograms. Heart 7: 353-370, 1920.

First Int’l. IEEE EMBS Special Topic Conf. on Microtechnology in Medicine and Biology October 12-14,2000, Palais des Congres , Lyon, France

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