Intelligent Acoustic Cardiology: Multi-Fractal Signal Processing and Machine Learning for Early Valvular Disease Detection

This book has systematically explored the intersection of advanced signal processing techniques and artificial intelligence (AI) methodologies in the early detection and management of cardiac pathologies. As cardiovascular diseases (CVDs) continue to be a leading global health challenge, the need for innovative, non-invasive diagnostic methods is more pressing than ever. The research has demonstrated that enhancing the analysis of phonocardiogram (PCG) signals through advanced signal processing not only improves the signal-to-noise ratio but also clarifies cardiac sounds, facilitating more accurate interpretations. By developing tailored machine learning algorithms, this work significantly contributes to the automation of pathological feature detection in PCG signals, paving the way for timely and precise diagnoses. The integration of AI with conventional cardiac monitoring practices has been shown to enhance diagnostic accuracy and positively influence clinical outcomes. By proposing a comprehensive framework that incorporates these advanced techniques, this book lays the groundwork for a transformative approach to cardiac disease management, emphasizing the importance of timely clinical interventions.