Biomedical Signal Analysis for Connected Healthcare provides rigorous coverage on several generations of techniques, including time domain approaches for event detection, spectral analysis for interpretation of clinical events of interest, time-varying signal processing for understanding dynamical aspects of complex biomedical systems, the application of machine learning principles in enhanced clinical decision-making, the application of sparse techniques and compressive sensing in providing low-power applications that are essential for wearable designs, the emerging paradigms of the Internet of Things, and connected healthcare. Provides comprehensive coverage of biomedical engineering, technologies, and healthcare applications of various physiological signals Covers vital signals, including ECG, EEG, EMG and body sounds Includes case studies and MATLAB code for selected applications
This indeed confirms the potency of manually generated features competing favorably with deep learning-based features as ... the first difference of phase and the normalized energy features from EEG signals which were decomposed into ...
It also presents the concepts of the Internet of Things, the set of technologies that develops traditional devices into smart devices. Finally, the book offers research perspectives, covering the convergence of machine learning and IoT.
The new edition of this book includes: End-of-chapter study questions, problems, and laboratory exercises Details on the z-transform, the Fourier Transform, random processes, and linear filters and their characteristics Methods for analysis ...
A brief introduction to EEG signal has been covered in Chapter 1, Section 1.2. The intention here, however, is to show detailed and practical applications for the reader to get an insight on how to process EEG signals using deep ...
This work is edited by Prof. Lotfi Chaari, professor at the University of Sfax, and previously at the University of Toulouse. This work comes after more than ten years of expertise in the biomedical signal and image processing field.
A unified overview of the field, this book explains how to properly use signal processing techniques for biomedical applications and avoid misinterpretations and pitfalls.
The authors have developed this book with graduate and post graduate students in mind, making sure they provide an accessible entry point into the field.
Key Features: Presents examples of state-of-the-art health monitoring systems using IoT infrastructure Covers the full spectrum of physiological sensing, data acquisition, processing, and data security Provides relevant example applications ...
The book contains peer-reviewed proceedings of the International Conference on Emergent Converging Technologies and Biomedical Systems 2021.
This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need a cogent resource on the most recent and promising machine learning techniques for biomedical signals analysis.