Stratification of time-frequency abnormalities in the signal-averaged high-resolution ECG in postinfarction patients with and without ventricular tachycardia and congenital long QT syndrome.
ABSTRACT Having developed sound mathematical techniques that allow precise mapping of cardiac signals in the time-frequency (TF) and time-scale planes, the next important issue is to extract from these representations information that best reflects the electrophysiologic and anatomic derangement unique to patients at risk of arrhythmias and other cardiac diseases. In this study, the authors present a new method that stratifies the magnitude of the TF transforms of abnormal cardiac signals into distinguishing features by comparing the means of the coefficients of the TF transforms of any study population to the corresponding means of a control population using a standard ANOVA technique. This results in a three-dimensional mapping of the high-resolution ECG into time, frequency, and P value components. Significant energy increases are given positive P values and depressed energies are given negative P values: these are ranked according to a color scale. The method was tested on two study populations: postmyocardial infarction patients with documented ventricular tachycardia (MI+VT, n = 23) and without (MI-VT, n = 40) and patients with congenital long QT syndrome (LQTS, n = 19). Two groups of healthy control subjects (n = 31 and n = 40) were used as a reference group matched for sex. The study results were based on the Morlet analyzing wavelets, with frequencies ranging from 40 to 250 Hz in 10 logarithmically progressing scales, and computed millisecond per millisecond over a 350-ms analyzing time window, starting from 100 ms before the onset of the QRS. The patients with MI+VT displayed significantly increased high-frequency components in the 40-250-Hz frequency range, corresponding to prolonged QRS duration and late potentials in the area from 80 to 150 ms after QRS onset. Significantly depressed energy (P < 10(-4)) was also observed for the 40-106-Hz frequency range in the first 50 ms of the QRS complex, mainly in lead Y and in the magnitude vector. In patients with LQTS, significant modifications (P < 10(-2)) were observed in the first half of the QRS and in the ST-segment, in all leads, revealing anomalies in the genesis of the ventricular depolarization and repolarization processes. In conclusion, the authors propose a new method for the stratification of abnormal TF components occurring in the signal-averaged high-resolution electrocardiogram of patients at risk of VT and fibrillation under different pathologic conditions.
- Annals of Noninvasive Electrocardiology 10/2006; 3(1):54 - 62. · 1.08 Impact Factor
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ABSTRACT: Background:Wavelet representation is able to detect low amplitude patterns even if hidden within signals of much higher amplitudes.Method:A software system has been developed that implements wavelet representation of signal-averaged electrocardiograms (SAECG). In this system, wavelet analysis leads to 4 numerical parameters that characterize the content of low amplitude perturbations found within the high gain QRS complex. In three substudies, these numerical parameters were compared with the standard time-domain indices of SAECG.Populations:Normal limits were identified from recordings of 104 normal healthy volunteers (54 males, mean age 50 ± 17 years). Short-term reproducibility of the numerical indices and of abnormal findings was evaluated in a population of 85 subjects (16 healthy volunteers, 22 patients with documented ventricular tachycardia [VT] without structural heart disease, 30 patients with documented sustained postinfarction VT, and 17 survivors of acute myocardial infarction) who were each recorded three times with 5-minute periods separating individual recordings. The power of wavelet and time-domain analyses in distinguishing patients with and without sustained VT after myocardial infarction was assessed using recordings of 53 patients with postinfarction VT and of 53 age, sex, and infarct site matched patients without a history of arrhythmic complications after infarction.Results:The studies have shown that (a) the indices of wavelet analysis are more reproducible than the time-domain indices, (b) the distinction between patients with and without VT after myocardial infarction is similarly powerful by wavelet and time-domain analyses, and the association of the positive SAECG analysis with postinfarction VT is highly significant with both analyses (P = 3.94 × 10–14 for wavelet analysis and 2.55 × 10−9 for time-domain analysis), the indices of wavelet analysis differ significantly between normals and patients with an uncomplicated history of myocardial infarction (P = 0.02–0.005), while time-domain indices do not (all parameters NS), (d) in contrast to the time-domain analysis, wavelet analysis was similarly powerful in identifying VT patients with anterior and inferior infarction (P = 1.4 × 10−9, n = 30, and P = 2.0 × 10−15, n = 23, respectively).Conclusion:Wavelet analysis is a highly reproducible method for SAECG processing which (a) is as powerful as the time-domain analysis for the identification of ischemic VT patients, (b) compared to the time-domain analysis, is not dependent on infarct site, and is able to distinguish postmyocardial infarction patients without VT from healthy subjects. A.N.E. 2000,5(1):4–19Annals of Noninvasive Electrocardiology 10/2006; 5(1):4 - 19. · 1.08 Impact Factor
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ABSTRACT: The human cardiovascular system exhibits complicated dynamics. Various signal analysis techniques have been utilized to study electrocardiogram (ECG) rate and morphology for their potentials in diagnosis and prediction of cardiovascular diseases. Conventional methods of linear signal theory, including power spectral analysis and time-domain analysis, have been widely adopted to analyze heart rhythm disorders due to cardiomyopathy and fibrillation. However, the success of these techniques is often hampered by the nonstationary and nonlinear character of the heart's activities. To overcome these limitations, many techniques based on nonlinear dynamics and chaos theory have been developed. In this work, we investigate various existing techniques for their potential application in the diagnosis of rhythm disorders. This comparison of different methodologies can be used as a guideline in selecting proper ECG signal analysis techniques.01/2009;