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