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 ...
Introductory sections on signals, systems, and transforms make this book accessible to students in disciplines other than electrical engineering.
This book offers an overview of a range of proven and new methods, discussing both theoretical and practical aspects of biomedical signal analysis and interpretation.After an introduction to the topic and a survey of several processing and ...
Covering the latest cutting-edge techniques in biomedical signal processing while presenting a coherent treatment of various signal processing methods and applications, this second edition of Practical Biomedical Signal Analysis Using ...
Market_Desc: The book is directed at engineering students in their final year of undergraduate studies or in their graduate studies.
J. Epilepsy 11(5), 303–309 (2002) M. Golmohammadi, A. Torbati, S. Diego, I. Obeid, J. Picone, Automatic analysis of EEGs using big data and hybrid deep learning architectures. Front. Hum. Neurosci. 13 (2019) I. Goodfellow, Y. Bengio, ...
This book covers all aspects of signals, from acquisition, the use of hardware and software, analyzing signals, and making use of AI in problem-solving.
The development of techniques to analyze biomedical signals, such as electro-cardiograms, has dramatically affected countless lives by making possible improved noninvasive diagnosis, online monitoring of critically ill patients, and rehabilitation...
In addition to examining techniques for electrical signal analysis, filtering, and transforms, the author supplies an extensive appendix with several computer programs that demonstrate techniques presented in the text.
The book not only covers the current techniques of biomedical signal processing, but it also offers guidance on which methods are appropriate for a given task and different types of data.The first several chapters o