Biomedical Signal Analysis and Processing: A Comprehensive Guide

This book, authored by Ganesh R. Naik, delves into the theoretical foundations and practical applications of biomedical signal analysis and processing. It provides a detailed overview of the nature of common biomedical signals, including electroencephalography, electromyography, and electrocardiography, among others.

Theoretical Basis of Biomedical Signal Processing

The book summarizes the theoretical basis of linear signal processing, encompassing both continuous and discrete representations, linear filters, and convolutions. It also covers Fourier and Wavelet transforms, essential tools for signal processing.

Machine Learning in Biomedical Signal Processing

Machine learning concepts are thoroughly explored, from classical methods to deep neural networks. This section prepares readers for the application of advanced data science techniques in neuroscience.

Applications in Neuroscience

The book presents several applications in neuroscience, focusing on diagnosis and therapy. It discusses various uses of biomedical signal processing in neurosciences, cardiovascular, and muscle-related fields, highlighting the potential of computational intelligence and machine learning in these areas.

Deep Learning and Biomedical Signal Processing

A comprehensive review of the theoretical basis of deep learning and its applications in biomedical signal processing and analysis is provided. This section is crucial for researchers and students seeking to understand the latest advancements in the field.

Target Audience

This book is designed for researchers, graduate students, and professionals in biomedical signal processing, signal processing, electrical engineering, neuroscience, and computer science. It serves as a valuable resource for those looking to gain a deeper understanding of biomedical signal analysis and processing.

Key Features

This book offers several key features, including:

• A clear explanation of signal processing in neuroscience applications using modern data science techniques.
• A comprehensive review of the nature and acquisition aspects of biomedical signals.
• A focus on selected applications in neurosciences, cardiovascular, and muscle-related biomedical areas.
• Coverage of computational intelligence, machine learning, and biomedical signal processing and analysis.
• A detailed review of the theoretical basis of deep learning and state-of-the-art biomedical signal processing and analysis.

Overall, this book provides a thorough understanding of biomedical signal analysis and processing, making it an indispensable resource for those working in this field.