EEG Signal Classification Using Bayesian-Optimized Neural Networks in IoMT Systems

The Internet of Medical Things (IoMT) consists of interconnected devices and applications that enable real-time collection, transmission, and analysis of medical data for healthcare applications. This study utilizes medical data from the publicly available BCICIV2a dataset rather than data collected...

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Vydáno v:Journal of computing and information technology Ročník 33; číslo 2; s. 109 - 122
Hlavní autor: Alnaily, Rana Raad Shaker
Médium: Journal Article Paper
Jazyk:angličtina
Vydáno: Sveuciliste U Zagrebu 01.06.2025
Sveučilište u Zagrebu Fakultet elektrotehnike i računarstva
University of Zagreb Faculty of Electrical Engineering and Computing
Témata:
Algorithms
Analysis
Artificial intelligence
Back Propagation Neural Network (BPNN)
Bayesian optimization algorithm
Electroencephalogram
Electroencephalography
Embedded systems
Epilepsy
Iraq
Machine learning
Mathematical optimization
Medical advice systems
Medical care
Medical informatics
Methods
Neural networks
Seizure, Internet of Things
Seizures (Medicine)
Signal processing
Technology application
Iraq
ISSN:1330-1136, 1846-3908
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Popis
Shrnutí:The Internet of Medical Things (IoMT) consists of interconnected devices and applications that enable real-time collection, transmission, and analysis of medical data for healthcare applications. This study utilizes medical data from the publicly available BCICIV2a dataset rather than data collected directly from individuals or medical institutions. With advancements in neuroinformatics and intelligent computing, the classification of electroencephalography (EEG) signals has become increasingly important, particularly for detecting and predicting epilepsy. However, existing EEG classification methods often suffer from low accuracy, high computational complexity, and slow processing. To address these challenges, this study proposes an EEG classification approach utilizing a Backpropagation Neural Network (BPNN) enhanced with Bayesian optimization. This method enhances the identification and prediction of epileptic seizures by utilizing IoT-enabled EEG data. Performance evaluation on the BCICIV2a dataset demonstrates that the proposed model achieves an accuracy of 93.21%, outperforming conventional techniques. The results indicate that this approach enhances efficiency and accuracy in EEG signal processing, contributing to real-time medical diagnostics. The integration of IoMT with advanced neural networks represents a significant advancement in medical informatics and telemedicine, providing promising directions for future research and clinical applications.
Bibliografie:336001
ISSN:1330-1136
1846-3908
DOI:10.20532/cit.2025.1005997