TIlME-FREQUENCY A&AI,YSIS OF HEART RATE VARIABILITY SIGNALS IN PATIENTS; WITH AUTONOMIC: DYSFUNCTION

M. V. Kamath, T. t3entley, R'. Spaziani, G. Tougas, E. L. Fallen, N. McCartney, J . Runions, and A. R. M. Wpton.

Departments of Medicine, Nursing, Kinesiology and Electrical and Computer Engineering, McMaster Univemity , Hamilton, Ontario, Canada.

ABSTRACT An imbalance in the autonomic nervous system has been suspected in patients with coronary artery disease during episodes of silent ischemia. Frequency analysis of the beat-to- beat heart rate variability (HRV) signals reveals a signature of autonomic regulation of the heart. We performed time-frequency analysis of HRV records containing silent ischemic episodes. In 14 out of 17 (82%) H[RV data sets there was a loss of power during such intervals, as determined by Si-T segment changes. Our studies suggest t h a t if the frequency content of the signal is well preserved, there is less likelihood of ischemic events of any appreciable duration.

INTRODUCTION The autonomic nervous system (ANS') not only governs important regulatory protzsses in major organ systems but is implicated in the integration of biophysical events between organs. Autonomic dysfunction is a consequence of or a precipitation in a number of diseases such as ischemic heart disease, congestive heart failure, gastroesophageal reflux disease, functional bowel syndrome, diabetic autonomic neuropathy etc. A computation of the time and frequency domain indices from the heart rate varialbiliky (HW) signal (derived from the ECG) is now acknowledged as a standard noninvasive procedure for examining the efferent autonoimic pathways subserving beat to beat GLrdlQVaSCular

function. Power spectral analysis of HRV recorded over a physiologically stable and stationary interval lasting 2-5 minutes, yields two specific peaks signifying sympathetic and vagal modulation of the heart. It is now generally accepted that the ratio of the low frequency peak (0.04-.15 Hz) to the high frequency p a k (vagally mediated and seen in the range 0.15.4 Hz) yields a signature of sympathovagal balance and that the heart serves as a convenient window through which the function of the ANS can be studied[l,2]. However, with unrestrained ambulatory (Holter ECG) monitioring even a short 2 to 5 minute heart rate variability data set may be insufficient for detection of important transients in the ECG record. Recently, applications of time-frequency algorithms, such as Wigner- Ville distribution have offered a novel approach to the study of frequency content of the heart rate signal that is changing in time [3].

Silent myocardial ischemia, defined by characteristic ST segment depression represents one such transient in the ambulatory ECG record.It has been suggested that autonomic imbalance may play a role in the triggering episodes of silent ischemia. We wanted to determine whether measurable changes in the power spectral indices accompany episodes of silent ischemiia. Because of the transient nature and nonstationary conditions of the ischemic process, we applied time-frequency power spectral analysis to ambulatory ECG records obtained from patients with recently documented coronary artery disease.

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MATERLAW AND METHODS We chose relatively uniform group of 22

patients , all of whom had angiographic evidence of atleast one major coronary artery stenosis 2 7 0 % . All were male, age 5 8 k 2 years. None were on beta blockers and all patients had 2 sets of Holter recordings, once on the day of the coronary angiogram (CA) and a second Holter immediately following the CA. From our group of 22 patients 2 patients had to be discarded because of poor signal quality. Each of the 20 records available for study contained at least one segment of silent myocardial ischemia defined as ST depression > 1.5 mm for at least 80 ms and persisting for a minimum of 2.0 minutes. From each record a pair of 1 hour segments were selected randomly. Of the 20 records, 3 contained ectopics making them unsuitable for further analysis. This left 17 records each with a high enough quality signal suitable for analysis. Eight of these contained an episode of ischemia varying from 2.0-8.5 minutes. The remaining 9 were free of ischemic episodes and ST segment changes. These 17 tapes were subsequently digitized by a qualified Holter technician using a Holter scanner medilog Excel, Oxford Medical Ltd. Oxon, UK] annotated, downloaded into a binary format onto a disk and analyzed in a blinded fashion. The sampling rate of the ECG signal was 125 samples/second.

SIGNAL PROCESSING OF FIEART RATE

Knowing the time onset of each record a tachogram of sequential R-R intervals was obtained. A beat-to-beat heart rate series was computed from the successive RR intervals and the resulting heart rate was sampled at 2 Hz using linear interpolation to obtain an equally sampled time series. Since the beat-to-beat heart rate signal is autonomically modulated in a dynamic environment and may give rise to transient changes, time- frequency analysis was applied in the form of Wigner-Ville Oyv)

VARIABILITY SIGNALS

analysis. The WV maps a one-dimensional time series into a two-dimensional function of time and frequency and permits one to capture the instantaneous changes in the power spectral characteristics with time [4]. For a real signal such as the HX time series, a Hilbert Transform is computed and cross terms are minimized by a Gaussian frequency smoothing window. The technique was validated by computing the WV distribution of a 10 minute continuous HR signal recorded from a 42 year male and containing segments of regulated breathing at 0.2 Hz and 0.33 Hz. Figure 1 displays a WV distribution of the signal. The figure depicts a moving window of HF spectral changes during the metronome driven breathing in a three dimensional fashion. The changing frequency content of the signal can also be viewed as a contour map (fig.2) of the same spectrum.

RESULTS Figure 3 shows an example of a 11.2 minute

segment containing an episode of ischemia. Note is made of the abrupt loss of power for 4 minutes in a time frequency plot. This corresponded to the approximate time of onset of ST depression and its duration. Note is made of two distinct episodes of tachycardia in the R- R interval series, one of which appears to coincide with a high LF power while the other lags slightly behind the onset of ST depression. Throughout this segment there is a dominant LF power with virtually absent vagal input (HF power). Variations in the time-frequency LF power attest to the nonstationarity of the signal even over short time periods during ambulatory recordings. Using the loss of power as a marker of ischemia, the concordance between the time frequency analysis and ST segment depression was 14 out of 17 (82%). Six of the eight segments containing the ischemic episodes were correctly identified by virtue of a clear loss of spectral power in the time-frequency plot. Eight of the nine segments not containing any ST depression were correctly identified

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Figures 1 (top) and 2: Wigner-Ville distribution and the contour map of a 10 minute heart rate variability signal segment showing the effect of different breathing frequencies. Initial two minutes of data with spontaneous breathing are followed by a 2 minute segment of breathing frequency (metronomed) at 0.2 Hz, a rest period with spontanmus breathing and by a 2 minute segment of metronomed breathing at 0.33 Hz.

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using the same criteria. In this small sample size there is the suggestion that as long as the frequency content of the signal is well preserved, there is less likelihood of ischemic event of any appreciable duration. We are processing more tapes to test these time frequency markers of ischemia on a larger sample size.

We found that loss of power in the time- frequency distribution of the beat-to-beat heart rate variability signal corresponds to time domain markers of silent ischemia as seen in the Holter ECG signal recorded from patients with coronary artery disease.

SUMMARY

Acknowledgements: This study was supported in part by grants from the DeGroote Foundation, the Heart and Stroke Foundation of Ontario and Natural Sciences and Engineering Research Council (NSERC) of Canada. Correspondence: kamathm@fhs. mcmaster. ca

REFERENCES 1.MV Kamath and.EL Fallen. CRC Crit.Rev. Biomed.Engn.2 1 :245, 1993. 2.Malik, AJ Camm. (Eds.) Heart Rate Variability, Futura Press, NY, 1995. 3.V Novak, P Novak, J Dechamplain et al. J. Appl. Ph ysiol. 74: 62 7, 1993. 4.B Boashash (Ed.) Time-Frequency Analysis, Longman Cheshire, 1992, pp. 163.

Figure 3. Wigner-Ville distribution of a 11.2 minute Heart Rate variability signal segment containing an ischemic episode of 4 minutes. Note the loss of power during the ischemic episode which was identified by tachycardia and ST segment changes in a Holter ECG record.