An Overview on Time-Frequency Effects of ECG Signals Using Synchroextracting Transform

Cardiac dysfunctions and arrhythmias are nonlinear and complex phenomena and can be monitored using electrocardiogram (ECG) recordings. ECG signals, and their underlying signal generation mechanisms, have strong nonlinear characteristics and, in some cases, present rich dynamic responses. In this paper we aim to characterize the abnormalities found in patients with arrhythmias through a novel signal processing procedure applied to ECG signals, and by characterizing them in the time and frequency domains. Specifically, we propose use of the wavelet-based Synchroextracting transform (WSET), an emerging method for time-frequency analysis (TFA). The central idea of WSET is to increase the concentration of energy in the time-frequency representation (TFR) and capture variations of the instantaneous frequency (IF) of the original, weak signal, which enables better characterization of anomalies in the frequency domain. In this study, using a public arrythmia database, WSET is employed to extract nonlinear and complex features of pathologies present in the ECG signals, thus facilitating characterization and diagnosis of subtle anomalies in the patient’s heart. The initial results obtained, based on the analysis of signals obtained from the MIT-BIH Arrhythmia dataset, demonstrated the ability of the new signal processing technique to detect short transients and subtle changes in the frequency spectrum of ECG signals.