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
Mục lục
Preface
Acknowledgements
Editor biographies
List of contributors
Contributor biographies
1 Introduction to AI-driven diagnostics and human-machine interfaces
2 Recent advancements in emerging technology for healthcare management systems
3 The role of high-performance computing in processing electronic healthcare records
4 Detection of attention deficit hyperactivity disorder using electroencephalogram signals: a review
5 Artificial neural network-based classification of eye states using electroencephalogram signals: a comparative analysis of algorithms and artifact removal techniques
6 Hybrid reptile search algorithm-snake optimizer and rational wavelet filter banks for Alzeihmer’s disease detection
7 Mother tree optimization for early detection of focal seizure using entropy-based features
8 Automatic detection of seizure activity using EEG signals
9 Prediction of rhythm-based abnormalities in electrocardiograms using time-frequency representations
10 Real-time implementation of ECG beat identification using Hilbert transform and aritificial neural network
11 Simulation and review of blood smear image-based leukemia classification using machine learning methods
12 Subject-independent emotion classification using galvanic skin response and electroencephalogram data
13 Speech emotion recognition using empirical wavelet transform and cubic support vector machine
14 Spectral and spatial analysis of EEG signals for imagined speech recognition
15 Classifying human attention states in EEG-based brain-computer interfacing using singular spectrum analysis
Giới thiệu về tác giả
Smith Khare is an Assistant Professor in the SDU Applied AI and Data Science, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Denmark, and worked as a Postdoctoral researcher in the Aarhus University, Denmark. He received his doctoral degree in Electronics and Communication Engineering at the Indian Institute of Information Technology, Design and Manufacturing Jabalpur (IIITDMJ), India in 2022. He has authored more than 50+ research papers in various reputed international Journals such as IEEE Transactions. Smith is listed in the top 2% Scientist in the World (2023, 2024), according to Elsevier.
Sachin Taran is an Assistant Professor in the Department of Electronics and Communication Engineering at Delhi Technical University, New Delhi, India. He has done postdoc research at the Nanyang Technological University (NTU) Singapore. His research interests include artificial intelligence, signal processing and time-frequency analysis. He is a fellow member of IETE, member of IANG and Associate Editor of Frontiers in Signal Processing. Since 2020, he has continuously awarded by Commendable Research Award in Delhi Technological University. He has authored more than 55+ research papers in various reputed international Journals and conferences.
Ankush D. Jamthikar is a postdoctoral research associate in the Division of Cardiovascular Disease and Hypertension at Rutgers University, Robert Wood Johnson Medical School, New Jersey. He has authored over 50 international journal papers, conference proceedings, and book chapters, focusing on cardiovascular disease (CVD) and stroke risk stratification, as well as artificial intelligence. He serves as an editorial board member for AI in Health and a guest editor for the MDPI journal, while also acting as a peer reviewer for several high-impact journals. Jamthikar has an h-index of 24 and an i-index of 34, with over 1, 300 citations to his work.
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