The book explores the application of artificial intelligence across various human-machine interfaces, addressing areas such as human attention, emotions, seizures, Alzheimer's disease, focal and non-focal disorders, electrocardiogram rhythms, abnormal heartbeats, and leukemia. It provides a thorough examination of techniques for analyzing and processing both physiological and physical signals, as well as smear blood images. Physiological signals discussed include electroencephalograms (EEG), electrocardiograms (ECG), and electronic health records (EHR), while physical signals encompass human speech. Serving as a comprehensive guide, the book delves into advanced signal processing techniques and the use of machine learning and deep learning for automated signal pre-processing and classification.

Key Features

Comprehensive review of the latest trends in physiological healthcare analytics for disease diagnostics
In-depth analysis of healthcare and major clinical applications using state-of-the-art AI techniques
Application of advanced and adaptive signal analysis methods for improved diagnostics
Integration of AI and transfer learning applications in healthcare
Contributions from highly cited researchers in their respective fields
Chapter content includes summaries, objectives, outcomes, worked examples, and multimedia
Extensive references are provided at the end of each chapter to support further research and study

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