Segmentation and identification of some pathological phonocardiogram signals using time-frequency analysis

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Segmentation and identification of some pathological phonocardiogram signals using time-frequency analysis

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Heart sounds that are multicomponent non-stationary signals characterise the normal phonocardiogram (PCG) signals and the pathological PCG signals. The time-frequency analysis is a powerful tool in the analysis of non-stationary signals especially for PCG signals. It permits detecting and characterising abnormal murmurs in the diagnosis of heart disease. In this study, the authors introduce a novel method based on time‐frequency analysis in conjunction with a threshold evaluated on Rényi entropy for the segmentation and the analysis of PCG signals. The method was applied to different sets of PCG signals: early aortic stenosis, late systolic aortic stenosis, pulmonary stenosis and mitral regurgitation. The analysis has been conducted on real biomedical data. Tests performed proved the ability of the method for segmentation between the main components and the pathological murmurs of the PCG signal. Also, the method permits elucidating and extracting useful features for diagnosis and pathological recognition.

Inspec keywords: patient diagnosis; phonocardiography; medical signal processing; diseases; feature extraction; time-frequency analysis; entropy

Other keywords: multicomponent nonstationary signals; systolic aortic stenosis; abnormal murmur; pathological phonocardiogram signal; Renyi entropy; signal segmentation; time-frequency analysis; heart disease diagnosis; pulmonary stenosis; signal identification; pathological PCG signal; feature extraction; heart sound; mitral regurgitation

Subjects: Signal processing and detection; Sonic and ultrasonic radiation (medical uses); Biology and medical computing; Sonic and ultrasonic radiation (biomedical imaging/measurement); Patient diagnostic methods and instrumentation; Digital signal processing

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