Intelligent Iot-Based Diagnostic and Assistive Systems for Neurological Disorders

Erscheinungsjahr 2026
ISBN: 978-0-443-34133-5
Verlag: Elsevier Science

Buch, Englisch, 300 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 450 g

Intelligent IoT-based Diagnostic and Assistive Systems for Neurological Disorders discusses the latest developed methods in IoT and its applications in neurological disorders that emphasize end-user requirements. Intelligent IoT is used to explore the intersection between medicine, data science, biomedical engineering, and healthcare systems. A comprehensive overview of modelling and analyzing the requirements of people with neurological disorders is presented in this book. Signals and images of biological activity are collected and analyzed based on patient specifications to facilitate more accurate diagnosis and treatment. The book also discusses cutting-edge AI methods for IoT devices designed to treat neurological conditions.

Autoren/Hrsg.

Heidari, Hanif

Murugappan, Murugappan

Fachgebiete

• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Biotechnologie
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

1. Review on IoMT Applications for Advancements in Oral Cancer
2. Review on various IoT Technologies to Assess and Diagnose Parkinson's Disease
3. Intelligent IoMT wearable technology for Neurological disorder diagnostic systems
4. Internet of Medical Things (IoMT) and AI in Predictive Diagnosis of Neurodegenerative Disorders: Global Innovations with an Indian Case Study
5. AI-Enabled Biosignal-Driven Edge-IoT Architectures for Secure Neurological Disorder Diagnosis
6. Chest respiratory classification by quantum regression neural network
7. Analyzing Sports Activity and Neurodegenerative Disease Progression Through IoT and Video Data Validation Methods
8. Comparative Study on Bark, Mel, and ERB Spectrum for Dysarthria Speech Classification Using Hybrid Convolutional Neural Network
9. Objective Analysis of Anti-social Behavioral Tendencies in Ketum-Dependent Subjects Using EEG Signals
10. Pain Prediction and Biomarker Analysis Using State-of-the-Art Deep Learning Methods with EEG
11. Investigation of Emotional Responses in Children with Autism Spectrum Disorder Using Frequency Domain Analysis of ECG Signals
12. Neurological Disorders and Sleep Duration: Impact on Health Using Machine Learning Techniques
13. AI-Enabled IoT Framework for Early Detection of Alzheimer’s Disease in Elderly Populations
14. Identification of receptive communication strategies for deaf adults without early intervention using electroencephalogram signals
15. Improving access and early detection of cognitive impairment with computational approaches to MCI screening
16. IoMT Enabled Audio Data Augmentation for Dysarthria Severity Detection Using SMOTE and CNN Architectures

Über Autor(innen)

Murugappan, Murugappan
Professor Dr. M. Murugappan is a Full Professor in Electronics at Kuwait College of Science and Technology (KCST) since 2016. Additionally, he serves as a Visiting Professor at the School of Engineering at Vels Institute of Science, Technology, and Advanced Studies in India, and an International Visiting Fellow at the Centre of Excellence in Unmanned Aerial Systems at Universiti Malaysia Perlis in Malaysia.

He holds an M.E. degree in Applied Electronics from Anna University, India, and received his Ph.D. in Mechatronic Engineering from Universiti Malaysia Perlis in 2010. Between 2010 and 2016, he worked as a Senior Lecturer at the School of Mechatronics Engineering, where he taught courses related to biomedical and mechatronics engineering.

Professor Murugappan has an outstanding research record, with numerous awards and research grants. His research in affective computing has received significant funding from Malaysia, Kuwait, and the UK.

He has published over 140 peer-reviewed conference proceedings papers, journal articles, and book chapters. Professor Murugappan is also a member of the editorial boards of prestigious journals, including PLOS ONE, Human Centric Information Sciences, Journal of Medical Imaging and Health Informatics, and International Journal of Cognitive Informatics.

Furthermore, he actively contributes to various IEEE Transactions as a reviewer and holds leadership roles, such as Chair of the IEEE Kuwait Section's Educational Activities Committee.

Heidari, Hanif
Dr. Hanif Heidari is an assistant professor in the Department of Applied Mathematics, Damghan University, Damghan, Iran. His research interests are in quantum machine learning, time series classification and prediction, grey system theory, and theoretical aspects of metaheuristic optimization methods. With over twelve years of experience, his foundations have been published in the high quality interdisciplinary scientific journals. He has also had given more than 20 talks in international conferences and supervised 22 graduate students in applied mathematics and computer science. Hanif enjoys of international scientific collaboration for solving complex practical problems using mathematical methods.