In the past 40 years, the area of biomedical signal processing has undergone tremendous evolution in terms of methodology and techniques developed. Biomedical Signal Analysis for Connected Healthcare provides rigorous coverage of these methodologies in the form of five generations of techniques, starting with 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, application of machine learning principles in enhanced clinical decision making, and the application of sparse techniques and compressive sensing in providing low-power applications essential for wearable designs and the emerging paradigms of Internet of Things and Connected Healthcare. Provides comprehensive coverage of the biomedical engineering, technological, and healthcare applications of various physiological signalsCovers vital signals including ECG, EEG, EMG, and body soundsIncludes case studies and Matlab code for selected applications INDICE: 1. Types and characteristics of biomedical signals 2. Time-domain processing of biomedical signals 3. Spectral-domain analysis of biomedical signals 4. Wavelet analysis of biomedical signals 5. Time-frequency analysis of biomedical signals 6. Sparse and compressive sensing techniques for biomedical signals 7. Machine learning for interpreting biomedical signals 8. Wearables and Internet of Things for connected healthcare
- ISBN: 978-0-12-813086-5
- Editorial: Academic Press
- Encuadernacion: Rústica
- Páginas: 270
- Fecha Publicación: 01/06/2019
- Nº Volúmenes: 1
- Idioma: Inglés