Advanced Signal Enhancement Approaches for Real-Time Acoustic Cardiac Monitoring and Health Assessment Systems

Advanced cardiac monitoring systems have become essential in modern healthcare due to the increasing prevalence of cardiovascular diseases and the need for early diagnosis. This research focuses on advanced signal enhancement approaches for real-time acoustic cardiac monitoring and health assessment systems. Heart sound signals, also known as phonocardiograms, contain critical diagnostic information about cardiac activity; however, they are highly susceptible to noise from respiration, motion artifacts, and environmental interference. To address these challenges, this study explores advanced signal processing techniques including adaptive filtering, wavelet transform, empirical mode decomposition, and machine learning-based denoising. The proposed framework integrates real-time heart sound acquisition with intelligent enhancement algorithms to improve signal clarity and diagnostic reliability. Furthermore, the system enables automated health assessment by extracting meaningful cardiac features for classification and decision-making. The results demonstrate that advanced signal enhancement significantly improves the accuracy of cardiovascular monitoring systems, making them suitable for wearable devices, remote healthcare, and telemedicine applications. This work contributes to the development of intelligent biomedical systems capable of providing efficient, accurate, and continuous cardiac health assessment.