Wavelet analysis in problems of classification of ECG signals
N.K. Smolentsev, P.N. Podkur
TL;DR
This paper explores wavelet analysis of ECG signals, demonstrating that high-frequency components contain diagnostic information and presenting an automated system to classify ECGs of healthy and sick individuals.
Contribution
It introduces a novel automated classification system based solely on high-frequency wavelet components of ECG signals for health diagnosis.
Findings
High-frequency wavelet components carry important diagnostic information.
The classification system effectively separates healthy and sick ECGs.
Wavelet analysis enhances ECG signal interpretation.
Abstract
In this paper, the wavelet analysis is used to study the ECG signal. We show that the high-frequency wavelet components of the ECG signal contain information on the functioning of the heart and can be used in diagnosis. We describe the automated classification system that separates the ECG of sick and healthy persons using only a high-frequency ECG component.
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Taxonomy
TopicsECG Monitoring and Analysis · Non-Invasive Vital Sign Monitoring · Advanced Scientific Research Methods