A novel approach for cardiac pathology detection using phonocardiogram signal multifractal detrended fluctuation analysis and support vector machine classification

Autor: Hakkoum, K.N., Cherif, L. Hamza
Zdroj: Research on Biomedical Engineering; 20240101, Issue: Preprints p1-16, 16p
Abstrakt: Purpose: The aim of this study is to develop a reliable method for assisting doctors in the early detection and diagnosis of heart disease by analyzing normal and abnormal phonocardiogram signals (PCG) using multifractal detrended fluctuation analysis (MFDFA). Methods: The MFDFA technique is a model-independent method for uncovering the self-similarity of a stochastic process or autoregressive model, which allows for the extraction of the most important characteristics of the PCG signal. Results: These characteristics include time evolution of the local Hurst exponent (Ht), q-order mass exponent (tq), root mean square (RMS), q-order Hurst Exponent (Hq), q-order singularity exponent (hq), and q-order dimension exponent (Dq) also proved its effectiveness by 98.5075% when classifying its results in support vector machine (SVM). Conclusion: The proposed method was applied using MATLAB R2022b with record signals from PhysioNet and Michigan websites. The MFDFA technique appears to be promising in heart disease study.
Databáze: Supplemental Index
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