Key result
Time frequency signal analysis using the wavelet transform achieved better time localization of late potentials and enabled separation between healthy subjects and patients with sustained ventricular tachycardia.
Why the study?
Does time frequency signal analysis of ECG signals improve the detection of late potentials and separation between healthy subjects and patients with sustained ventricular tachycardia?
Does time frequency signal analysis of ECG signals improve the detection of late potentials and separation between healthy subjects and patients with sustained ventricular tachycardia?
Time frequency signal analysis, particularly the wavelet transform, provides better time localization for detecting late potentials in ECGs and can distinguish between healthy subjects and patients with sustained ventricular tachycardia.
May enhance ECG late potential detection; leaves open clinical utility for ventricular tachycardia risk stratification pending validation.
It is shown that time frequency signal analysis could be a useful technique in detecting late potentials (LP). In particular, the spectrogram, the Wigner distribution and the wavelet transform have been applied to ECG signals from patients with sustained ventricular tachycardia. Comparisons of the three algorithms reveals that much better time localization is achieved by the wavelet transform. Moreover, it is concluded that a separation between healthy subjects and patients could be obtained based upon time frequency analysis.
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Khadra et al. (1993) studied Sustained ventricular tachycardia. Time frequency signal analysis (wavelet transform, spectrogram, Wigner distribution) vs. Healthy subjects was evaluated on Detection of late potentials and time localization. Time frequency signal analysis using the wavelet transform achieved better time localization of late potentials and enabled separation between healthy subjects and patients with sustained ventricular tachycardia.
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