Machine Learning Models and Architectures for Biomedical Signal Processing

Machine Learning Models and Architectures for Biomedical Signal Processing by Suman Lata Tripathi is an indispensable guide for anyone working at the intersection of healthcare and AI. This authoritative volume captures the urgent need to convert raw physiological data into reliable clinical insight, presenting modern machine learning and deep learning approaches tailored specifically for biomedical signals.

Inside, you’ll find clear, practical explanations of preprocessing and feature extraction, convolutional and recurrent architectures, transformer-based models, hybrid pipelines, and strategies for handling noisy, imbalanced, and high-dimensional biomedical datasets. The author translates complex theory into applied solutions with illustrative examples drawn from ECG, EEG, EMG, and wearable sensor data, helping readers understand model selection, training dynamics, validation protocols, and performance metrics relevant to clinical environments.

Engineered for researchers, clinicians, data scientists, and engineers, this book balances rigorous methodology with real-world applicability. It explores ethical considerations, regulatory constraints, and deployment challenges faced by healthcare organizations, making it especially useful for teams designing diagnostic algorithms, remote-monitoring systems, and decision-support tools. Regional relevance is emphasized through examples and case studies suited to hospitals, universities, startups, and research centers across North America, Europe, Asia, and beyond.

Whether you’re building prototypes or validating production systems, Suman Lata Tripathi’s book equips you with the architectural insight and practical frameworks needed to accelerate innovation in biomedical signal processing. Add this essential resource to your professional library to sharpen your skills, streamline development, and translate biomedical data into meaningful clinical outcomes. Order your copy today and begin transforming signals into actionable healthcare intelligence.

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