Citation

Zia, Zahra and Ejaz, Ahsen and Lew, Kai Liang and Zheng, Cheng and Babale, Suleiman Aliyu and Kurniawan, Tetuko and Lee, Chia Shyan (2025) The Role of Electroencephalography in Advancing Sleep Research. International Journal on Robotics, Automation and Sciences, 7 (1). pp. 93-103. ISSN 2682-860X

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Abstract

Electroencephalography (EEG) is fundamental in sleep research, providing critical insights into cerebral activity and significantly contributing to the diagnosis of sleep disorders. This study examines recent progress in EEG-based sleep research, emphasizing cutting-edge methods for sleep staging and disease identification. The amalgamation of machine learning and deep learning methodologies, encompassing hybrid models such as CNN-LSTM, has markedly improved the precision of sleep stage categorization and automated analysis. Enhancements in signal quality and dependability, especially by improvements in artifact removal methods like wavelet-enhanced independent component analysis (ICA), have further advanced these developments. The implementation of multimodal strategies, wearable EEG technology, and AI-enhanced systems has broadened the sphere of sleep monitoring beyond clinical environments, rendering it more accessible and individualized. This article examines the use of EEG in detecting sleep disorders, including insomnia, obstructive sleep apnea, and narcolepsy, by identifying biomarkers and abnormalities in sleep architecture. Emerging research underlines the promise of clinical EEG, marking it as a transformational tool for both study and therapy. Nonetheless, obstacles persist in domains such as noise reduction, biomarker standardization, and scalability. Future directions include merging EEG with imaging modalities like fMRI, developing wearable technology, and employing advanced AI for individualized sleep health management. In particular, EEG is highlighted as a transformational and promising tool for promoting sleep medicine through novel, accessible, and effective solutions.

Item Type:	Article
Uncontrolled Keywords:	Electroencephalography (EEG), Sleep Stag-ing, Machine Learning, Sleep Disorders, Artifact Removal 9
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	26 Jun 2025 01:26
Last Modified:	26 Jun 2025 01:26
URII:	http://shdl.mmu.edu.my/id/eprint/14070

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