DSP Based ST-Segment Analysis: The Wavellet Approach

J. S. Sahambi S. N. Tandon R. K. P. Bhatt Electrical Engg., Center for Biomedical Engg., Electrical Engg.,

I. I . T. Delhi, Hauz Khas, New Delhi PIN 110 016

INDIA INDIA INDIA

I. I. T. Delhi, Hauz Khas, NewDelhiPIN 110016

I. I. T. Delhi, Haw ](has, New Delhi PIN 110 016

Abstract - A novel algorithm for ST-segment analysis is developed using the multiresolufion wavelet approach. The algorithm has been implemented on TMS320C25 based add-on DSP card to PC to provide the on-line analysis and display of ST-segment data. The performance of the system is evaluated using the standard ECG waveforms with different morphologies and heart rates in order to take into account the variability of the data encountered in the clinical environment. The results show that the average errors in the calculation of the ST- segment with different morphologies at heart rates of 60 BPM and 120 BPM are 2.0% and 3.3%, respectively. These errors are well within the acceptable limits for the clinical use. The proposed algorithm has a great potential to be a part of an integral ECG analysis system.

I. INTRODUCTION

The importance of ST-segment has been highlighted by Suzuki et al. [I] and Weisner et al.[2]. The irregularity of the ST-segment in the Exercise ECG is considered electrophysiologically significant because it is an indicator of the imbalance in the myocardial oxygen supply/demand ratio. Also, in the patients suffering fiom myocardial ischemia or infarction the ST-segment may become irregular. Accurate detection of ST-segment, thus, has special diagnostic value and needs more investigation to supplement the present techniques.

Locating ECG waveform fiducial points (QRS complex, J point, onset and offset of T wave) is 9 critical step in automated ECG analysis for amplitude and interval

points are used to analyze the ST-segment. The recent advances in Digital Sigpal Processing

hardware have provided a wide variety of digital signal processor chips for on-line implementation of new mathematical techniqut:s like Short Time Fourier Transform and Wavelets. The use of DSP hardware enables one to design systems which can work in real tirne in a clinical environment and provide on-line analysis.

11. WAVELETS

Wavelet transformation is a linear operation which decomposes a signal into components which appear ai: different scales (or resolutions) [5],[6],[7],[8]. The wavelet transform of a functilDn f ( t ) E L2(R) at scale a andl position z is given by

where Y(t) is the .mother wavelet which satisfies the admissibility conditions [8] and * denotes the complex conjugation. The motheir wavelet and its scaled versions act as band pass filters on the: signal. The wavelet we used is the first derivative of a smoothing function (Gaussian function) [41.

111. METHODOLOGY

measurements. The approach taken by Weisner et al. [2] for analyzing the ST-segment uses a hardware QRS detector which gives large errors when the signal is contaminated with noise. Any error in the location of QRS complex will result in errors in the ST segment analysis as the QRS complex is taken as reference. The techniques reported earlier for detecting these fiducial points were affected by the presence of noise like base-line wander and power fiequency interference. In addition, the conventional methods use R+x

The ECG is digitized at the rate of 230 samples per second with a 16 bit bipolar analog-to-digital converter with a dynamic range of -10 to +10 volts. The resolution of the system is 0.3 milli volts. The digitized data is analyzed by the TMS320C25 based DSP add-on card which is connected to a host PC. The results of analysis are stored and displayed. The analysis of ST-segment requires the detection of iso-electric level, onsets and offsets of QRS and the T wave.

and J+x approaches [ 1 J which are empirical and the value of x depends on the heart rates.

The paper describes a new technique for ST-segment analysis using wavelet transforms. The fiducial points of the ECG signal are determined with greater accuracy by using the wavelet transforms at selected scales [3][4]. These fiducial

A. Detection of Iso-electric level, onsets and cfsets of QRS Complex and T wave

The detection of Iso-electric level, onsets and offset of QRS complex and T wave are based on the modulus maxima (maximum absolute value) and zero crossings of the wavelet

455 0-7803-3869-3/97 10.0001 997IEEE

TABLE I PASS BANDS OF WAVELET FILTERS AT FOUR SCALES

Scale I Lower 3dBfreq. 1 Upper 3dBfreq. 1 f R

Fig. 1. (a) ECG waveform (b) Wavelet transform at scale 2l (c) Wavelet transform at scale 23 .

transforms at the characteristic scales [3],[4]. The wavelet transforms are computed only for the scales of interest. The use of a optimized wavelet reduces the effect of base line drift and power line interference on the timing characterization [4]. The pass bands of the optimized wavelet filters are given in the Table I

The iso-electric line lies between the offset of the P and the onset of the QRS complex. It can be observed from Fig. 1 that in scale 23 this segment is reflected in the flat portion between the offset of the P wave and the onset of the QRS complex. The algorithm searches for a segment which has a minimum (or zero) value of wavelet transform. The search is started from onset of the QRS complex and goes towards the offset of the P wave. Searching for this segment in scale 23 gives more reliable value because at this scale the noise is suppressed. The onset and offset of the T wave are computed by the zero crossing of the wavelet transform at scale 23 [3], [41.

B. Detection of Jpoint

J point is the first inflection after the S point and in some cases it is same as the S point. J point is detected by using the scale 2'. This point corresponds to the peak in scale 2' after the falling edge of the wavelet transform which crosses the zero line as shown in the Fig. 1.

R

Q s Fig. 2. ST-segment is the segment between the J point and the

T onset.

42.5 7.0 22.0

74 4 1 12 7

The conventional methods for the ST segment analysis are 1) J+x, 2) R+x, and 3) windowed approach. Here the J point is defined as the first inflection after the S point and insome cases it can be same as the S point. J+x and R+x are the methods commonly used by researchers and in commercial machines. The ST-segment is defined as part of ECG located between the points J and J+x ms. The typical measurement is made at J+80 ms (used by the Philips Viagraph). The Marquette machines allows the user to select the ST point at 0, 40, 60 or 80 ms after the J point. R+x method uses the R peak as the reference point.

The presence of noise can severely affect the performance of the above approaches. Additionally, the above formulas are derived from the physiological factors and can be influenced by noise. Also, at higher heart rates, i.e., in the third or fourth stage of Treadmill Test, the value of x must be made smaller to give correct results.

In the proposed window approach the ST-segment is defined as the segment between the J point and the onset of the T wave as shown in the Fig. 2. The end point of the ST- segment (T onset) is the inflection point between the J point and the T peak. The algorithm uses the windowed approach to locate this point 131. The window starts from the first inflection after S point and ends with the point where the slope of T wave is maximum. The search starts backward from the end of the window and the algorithm searches for a point where the value of wavelet transform at scale 23 is minimum (or zero). This is taken as the T onset. However, if this inflection point is not present, then the standard empirical formula is used for detection of T onset. The ST-segment is the interval between the J point and the T onset. This approach works good for different morphologies and heart rates.

C. Application of DSP hardware

In Exercise ECG the 6 or 12 leads are analyzed for ST segment. To accomplish this in real time the analysis by wavelet technique has been implemented on TMS 320C25 based add-on DSP card and the other house keeping tasks like display and storage of results are done by a 486 PC.

Iv. RESULTS

To evaluate the performance of the system standard ECG waveforms with different morphologies (ST value) were

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1-l

6

5

E 4 U 3 s

2

1

0 -0.5 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1

ST value (milli volt)

Fig. 3: Percentage error in ST segment for various values of ST level and heart rates. The peak errors for heart rates of 80 BPM and 120 BPM are 4.0% and 6.5%, respectively.

analyzed for ST-segment. The variation in the ST levels given in 1.0 milli volt ECG signal varied from -0.5 to 0.1 milli volts. The average error in the ST-segment duration was 2.0% for a heart rate of 80 BPM and 3.3% for a heart rate of 120 BPM which are well within the acceptable limits for the clinical use (Fig. 3 ) .

electric level, J point, onset and offset of QRS and T wave) in theECG waveform. These fiducial points are used to locate the ST-segment. The results show that the algorithm can accurately detect ST-segment with different moqphology and heart rates. The system has been implemented on one channel and can be modified for multi channel analysis.

REFERENCES

[31

[41

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V. CONCLUSIONS

A DSP (TMS320C25) based system is developed for analyzing the ST-segment in real time. The system uses thewavelet approach to detect the fiducial points (iso-

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