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Artificial Intelligence (AI) has started to grow in the field of medicine at a tremendous rate. AI is also becoming one of the strong modes of diagnosis for a wide range of conditions. The major benefits offered by AI would be diagnosing the conditions, automating and making enhancements in them, and bringing about the ability to offer continuous monitoring of patients. This, in turn, would lead to better health outcomes resulting from improved monitoring and diagnosis. Such AI systems require large and varied patient data from the real world to build their clinical solutions. The data may be physiological signals such as electroencephalogram (EEG) and electrocardiogram (ECG), or imaging modalities that range from computed tomography and magnetic resonance imaging.
This book intends to report on state-of-the-art AI applications in human-machine interfaces, investigations of human attention, emotions, seizures, Alzheimer's disease, focal and non-focal disorders, abnormal heart rhythms, and leukemia detection. The book encompasses discussions of the in-depth techniques for the analysis and processing of both physiological such as EEG, and ECG, electronic health records, and physical signals such as human speech.
The book offers an in-depth exploration of advanced signal processing techniques, with a strong focus on incorporating machine learning and deep learning methods. It covers the entire process, from signal pre-processing to the automatic classification and identification of various phenogroups. This makes it an invaluable resource for anyone seeking a comprehensive understanding of how AI is being applied in modern healthcare diagnostics.
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.
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
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