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