The proposed machine learning model for heart sound analysis achieved 99.5% accuracy in binary classification using 2 s signal segments.
Do recurrence plot-derived features and machine learning algorithms accurately classify heart sound recordings to detect cardiac abnormalities?
A machine learning framework using recurrence plots for feature extraction from phonocardiograms achieves high accuracy in classifying heart sounds, offering a potential automated screening tool.
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Early and reliable detection of cardiac disease is crucial for preventing complications and enhancing patient outcomes. Phonocardiogram (PCG) signals, which encode rich information about cardiac function, offer a non-invasive and cost-effective way to identify abnormalities such as valvular disorders, arrhythmias, and other heart pathologies. This study investigates advanced diagnostic methods for heart sound analysis to improve the detection and classification of cardiac abnormalities. In the proposed framework, recurrence plots (RPs) are used for feature extraction, while machine learning algorithms are applied for classification, creating a diagnostic model that can recognize cardiac conditions from composite acoustic signals. This method serves as an efficient alternative to more computationally intensive deep learning methods and other high-dimensional ML-based solutions. Experimental results demonstrate that the multiclass classification task achieves up to 98.4% accuracy, and the binary classification reaches 99.5% accuracy using 2 s signal segments. The techniques assessed in this research demonstrate the potential of automated heart sound analysis as a screening tool in both clinical and remote healthcare settings. Overall, the findings highlight the significance of machine learning in heart sound classification and its potential to facilitate timely, accessible, and cost-effective cardiovascular care.
Almosained et al. (Thu,) reported a other. The proposed machine learning model for heart sound analysis achieved 99.5% accuracy in binary classification using 2 s signal segments.