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.
[Show abstract][Hide abstract] ABSTRACT: The aim of this paper is to validate wavelet analysis of Holter
recordings as a tool for the detection of risk of sudden cardiac death
for patients surviving an acute myocardial infarction. The study uses
time-averaged HR-ECGs from the European Myocardial Infarct Amiodarone
Trial (EMIAT). Each HR-ECG is transformed into 511 orthogonal Meyer
wavelet coefficients, extending from 128 ms before to 384 ms after QRS
onset, and their value is assessed by means of the CARTEF Time-Frequency
Abnormalities Stratification method. We then perform a linear
discriminant analysis to assess the discriminant power of all wavelet
coefficients with a significant p-value (p<0.0001), and we compare
them to the clinical parameters and also to a combination of them
"The localization of the abnormal time–frequency components of the HRECG characterizing the VT risk in post-infarction patients was determined by a statistical method previously described  . Briefly, based on a standard ANOVA technique   this approach allowed stratification of the magnitude of the wavelet transforms into distinguishing features by comparing the means of the wavelet coefficients of the MI+VT population with the corresponding means of the MI-VT population. "
[Show abstract][Hide abstract] ABSTRACT: Late potentials (LPs) in the terminal portion of the QRS complex are commonly sought to identify post-myocardial infarction patients prone to ventricular tachyarrthythmias (VT) or sudden death. More recent time frequency signal processing tools have been shown to provide new parameters for the quantification of LPs and abnormal activities buried within the QRS complex.
The study population comprised 23 myocardial infarction patients with documented sustained VT (MI+VT), 40 myocardial infarction patients without VT (MI - VT) and 31 normal subjects. The reproducibility of the method was tested in an additional set of 66 patients. The signal-averaged high-resolution electrocardiograms (HRECGs) were quantified by deconstructing the unfiltered X, Y and Z leads using a 511-orthogonal wavelet network. Using receiver operating characteristics (ROC) curves and discriminant analysis applied to the wavelet coefficients, we extracted the most significant wavelets to classify the post MI patients. These wavelets detected time-frequency alterations both in the ST segment and within the QRS complex, characterizing patients prone to VTs. The same statistical methods were applied to the conventional time-domain measurements. The combined application in our population of the orthogonal wavelet deconstruction method and discriminant analysis had 91% sensitivity and 95% specificity, an improvement of 22% and 25%, respectively, compared with the conventional time domain method. Reproducibility was 82%.
In post-myocardial infarction patients, orthogonal wavelet transforms can detect alterations in high-frequency components within the QRS and ST segment. Our findings support the view that wavelet-related parameters are more relevant than those of the time domain method in predicting subsequent malignant tachyarrhythmias.
[Show abstract][Hide abstract] ABSTRACT: Clinical centers are increasingly using new techniques such as Holter QT, late potential, and wavelet measurements. However, we lack validated databases for the assessment of the performance of the signal-processing methods and their reproducibility. Failure of the QT interval to adapt to changes in the heart rate is considered to be a more meaningful parameter than QT prolongation itself. In this study, different factors that may affect the reproducibility of QT and QTm (onset of the QRS to the maximum of T) measurement are analyzed: the incidence of sympathetic tone and parasympathetic activity on low- and high-frequency QT variability, the very low frequency dependency of the QT interval to changes in the R-R interval, changes in the heart's position, and measurement errors. Typical root-mean-square values of the beat-to-beat measurement errors in upright-position Holter recordings are only 1.5 ms for QT versus 3.4 ms for QTm. Although the dependence of the QT interval on the heart rate is well established, the method for rate correction of the QT interval remains controversial. None of the formulas for heart rate adjustment of the QT previously proposed provide complete correction for all of the rate influences involved due to "memory phenomenon"; that is, there is a time delay, ranging up to 3-4 minutes, between a change in heart rate and the subsequent change in the QT interval. This problem has been solved by developing patient-specific neural networks that are trained to "identify" the dynamic behavior of the QT interval (or QTm) as a function of the R-R interval in order to predict the beat-to-beat changes of the QT interval as a function of the measured beat-to-beat changes of the R-R interval. Computing the differences between the predicted and the measured QT interval will allow for the detection of any significant deviations, both in the steady-state and transient conditions. Recent developments in the analysis of the high-resolution electrocardiogram (HRECG) in the time domain and frequency domain, with emphasis on the assessment of the reproducibility of late potential and wavelet measurements, are also reported in this study. The two main causes of variability in HRECG analysis are physiology and, for time-domain analysis, intermanufacturer variability. Physiologic changes can be overcome by standardizing the clinical protocols and repeating the recordings. The most important technical requirement for the proper use of late potentials is to standardize the algorithm for the detection of QRS offset among different late potential analyzing machines so that clinical data can be exchanged. The recently introduced wavelet transform provides a fruitful alternative to the more classical time-domain methods. Preliminary results show an 8 to 15% performance improvement over conventional time-domain analysis for the stratification of the HRECG after myocardial infarction. Reproducibility is excellent, up to 100%, but needs to be assessed on larger populations matched for age, sex, and pathology.
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