Hassan, Md Mahmudul and Islam, Md Nahidul and Norizam, Sulaiman and Hossain, Mirza Mahfuj and Mendes, Jorge M. (2025) Real-time EEG signal analysis for microsleep detection: Hyper-Opt-ANN as a key solution. IEEE Access, 13. pp. 66354-66372. ISSN 2169-3536. (Published)
|
Pdf
Real-time EEG signal analysis for microsleep detection.pdf Available under License Creative Commons Attribution. Download (4MB) | Preview |
Abstract
Microsleeps are brief lapses in awareness that pose significant risks, particularly in activities requiring continuous attention, such as driving. These episodes are common in sleep-deprived individuals and can lead to catastrophic outcomes. Electroencephalography (EEG) is a promising technique for detecting microsleeps due to its high temporal resolution, allowing real-time brain activity monitoring. The study aims to develop a lightweight version of the model to reduce computational costs and provide faster detection, enabling quicker intervention to prevent accidents in safety-critical environments. We propose a customized deep learning model, Hyper-Opt-ANN, designed to detect microsleep episodes from EEG signals. The model is evaluated across five time windows (1 second, 2 seconds, 3 seconds, 4 seconds, and 5 seconds), with the 4 seconds window showing the best performance. The Hyper-Opt-ANN model achieved a significant accuracy of 97.33%, demonstrating its efficacy and potential for accurate microsleep detection using EEG signals. This method significantly outperforms traditional approaches and has potential applications in safety-critical domains. This study demonstrates the feasibility of using EEG signals and advanced deep learning models for detecting microsleep and enhancing safety in high-risk environments.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | EEG signal; hyper-Opt-ANN; Microsleep detection; Parameter optimization; Time-window selection |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QP Physiology T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Electrical and Electronic Engineering Technology |
| Depositing User: | Mrs. Nurul Hamira Abd Razak |
| Date Deposited: | 08 Jul 2025 01:30 |
| Last Modified: | 08 Jul 2025 01:30 |
| URI: | http://umpir.ump.edu.my/id/eprint/45035 |
| Download Statistic: | View Download Statistics |
Actions (login required)
 |
View Item |