Sinusoidal modelling for efficient source coding of phonocardiogram signals in cardiac monitoring devices

Abstract

This study focuses on developing an efficient and cost-effective approach for compressing Phonocardiogram (PCG) signals without compromising their quality. The method utilizes two data compression techniques, capturing heart sounds and transforming them into the frequency domain to extract essential features such as frequency, phase, and amplitude peaks. The compressed signals are subsequently reconstructed to faithfully replicate the original heart sounds. The findings contribute to advancements in biomedical signal processing and compression methodologies, with potential applications in telemedicine and remote sustainable healthcare systems. Compressed PCG signals enable real-time remote consultations and continuous cardiac health monitoring, particularly in underserved regions with limited medical resources. This research holds significant potential for improving access to cardiovascular healthcare and promoting overall health and well-being.