Time-frequency analysis of normal and abnormal biological signals

Abstract Due to the non-stationary, multicomponent nature of biomedical signals, the use of time-frequency analysis can be inevitable for these signals. The choice of the proper time-frequency distribution (TFD) that can reveal the exact multicomponent structure of biological signals is vital in many applications, including the diagnosis of medical abnormalities. In this paper, the instantaneous frequency (IF) estimation using four well-known TFDs is applied for analyzing biological signals. These TFDs are: the Wigner–Ville distribution (WVD), the Choi–Williams distribution (CWD), the Exponential T-distribution (ETD) and the Hyperbolic T-distribution (HTD). Their performance over normal and abnormal biological signals as well as over multicomponent frequency modulation (FM) signals in additive Gaussian noise was compared. Moreover, the feasibility of utilizing the wavelet transform (WT) in IF estimation is also studied. The biological signals considered in this work are the surface electromyogram (SEMG) with the presence of ECG noise and abnormal cardiac signals. The abnormal cardiac signals were taken from a patient with malignant ventricular arrhythmia, and a patient with supraventricular arrhythmia. Simulation results showed that the HTD has a superior performance, in terms of resolution and cross-terms reduction, as compared to other time-frequency distributions.

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