Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing 1st Edition

Capture the future of cardiac care with Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing, 1st Edition by Rajesh Kumar Tripathy and Ram Bilas Pachori — a definitive, practical guide that transforms raw cardiovascular signals into clinically meaningful insights.

Delve into a clear, methodical exposition of modern techniques: from preprocessing and noise reduction of ECG and PCG signals to feature extraction using wavelets, time–frequency analysis, and advanced machine learning and deep learning models. Rich with real-world examples and algorithmic workflows, this book bridges signal processing theory and hands-on implementation for accurate arrhythmia detection, heart-sound classification, and wearable-sensor analytics.

Whether you are a biomedical engineer, data scientist, clinician, graduate student, or researcher, you will gain practical skills to build robust pipelines for remote monitoring, telemedicine, and automated diagnostics. The authors present performance evaluation metrics, case studies, and best-practice strategies that optimize model generalizability across noisy clinical environments and diverse patient populations — making it invaluable for healthcare innovation in India, Europe, North America, and beyond.

This edition stands out for its balance of mathematical rigor and application-oriented guidance, helping readers move from concept to deployable solutions. If you want to advance cardiovascular research or develop intelligent medical devices, this book is your blueprint.

Equip yourself with the tools shaping cardiac data science today. Order your copy of Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing and start turning cardiovascular signals into life-saving decisions.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.