Heartbeat Sound Classification Using Mel-Spectrogram and CNN Optimized by Frilled Lizard Algorithm for Cardiovascular Disease Detection

Abstract

Cardiovascular disease (CVD) continues to be the predominant cause of mortality globally, underscoring the critical necessity for prompt and precise diagnostic techniques.  This paper introduces an innovative machine learning framework for categorizing heartbeat sounds into four classifications—normal, murmur, additional heart sound, and artifact—utilizing audio recordings from the PhysioNet/CinC Challenge 2016 dataset.  The methodology employs Mel-Spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs) for feature extraction, converting raw heart sound data into comprehensive time-frequency representations.  A Convolutional Neural Network (CNN) is utilized for classification, with its hyperparameters refined by the recently developed Frilled Lizard Optimization (FLO) method, a bio-inspired metaheuristic that emulates the hunting and climbing behaviors of the frilled lizard.  The suggested method illustrates the capability of integrating deep learning with sophisticated optimization techniques to improve the diagnostic precision of cardiac auscultation, particularly in non-clinical and remote environments.  This can facilitate the development of intelligent, scalable, and proactive intervention methods in preventative cardiovascular healthcare.