Microsleep Predicting Comparison Between LSTM and ANN Based on the Analysis of Time Series EEG Signal
A microsleep is an unintentional, transient loss of consciousness correlated with sleep that lasts up to fifteen seconds. Electroencephalogram (EEG), recordings have been extensively utilized to diagnose and study various neurological disorders. This study analyzes time series EEG signals to predict...
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UTeM
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/40814/1/Microsleep%20Predicting%20Comparison%20Between%20LSTM%20and%20ANN%20Based%20on%20the%20Analysis%20of%20Time%20Series%20EEG%20Signal.pdf http://umpir.ump.edu.my/id/eprint/40814/ https://jtec.utem.edu.my/jtec/article/view/6322 |
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| author | Hasan, Md Mahmudul Hossain, Mirza Mahfuj Norizam, Sulaiman Khandaker, Sayma |
| author_facet | Hasan, Md Mahmudul Hossain, Mirza Mahfuj Norizam, Sulaiman Khandaker, Sayma |
| author_sort | Hasan, Md Mahmudul |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | A microsleep is an unintentional, transient loss of consciousness correlated with sleep that lasts up to fifteen seconds. Electroencephalogram (EEG), recordings have been extensively utilized to diagnose and study various neurological disorders. This study analyzes time series EEG signals to predict microsleep employing two deep learning models: Long-Short Term Memory (LSTM) and Artificial Neural Network (ANN). The findings show that the ANN model achieves outstanding metrics in microsleep prediction, outperforming the LSTM in key performance metrics. The model demonstrated exceptional performance, as demonstrated by the outcomes of the Scatter Plot, R2 Score, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Between the two models, the ANN model achieved the most significant R2, MAE, MSE, and RMSE values (0.84, 1.10, 1.90, and 1.38) compared to the LSTM model. The critical contribution of this study lies in its development of comprehensive and effective methods for accurately predicting microsleep events from EEG signals. |
| format | Article |
| id | my.ump.umpir.40814 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2024 |
| publisher | UTeM |
| record_format | eprints |
| spelling | my.ump.umpir.408142024-04-01T05:20:09Z http://umpir.ump.edu.my/id/eprint/40814/ Microsleep Predicting Comparison Between LSTM and ANN Based on the Analysis of Time Series EEG Signal Hasan, Md Mahmudul Hossain, Mirza Mahfuj Norizam, Sulaiman Khandaker, Sayma TK Electrical engineering. Electronics Nuclear engineering A microsleep is an unintentional, transient loss of consciousness correlated with sleep that lasts up to fifteen seconds. Electroencephalogram (EEG), recordings have been extensively utilized to diagnose and study various neurological disorders. This study analyzes time series EEG signals to predict microsleep employing two deep learning models: Long-Short Term Memory (LSTM) and Artificial Neural Network (ANN). The findings show that the ANN model achieves outstanding metrics in microsleep prediction, outperforming the LSTM in key performance metrics. The model demonstrated exceptional performance, as demonstrated by the outcomes of the Scatter Plot, R2 Score, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Between the two models, the ANN model achieved the most significant R2, MAE, MSE, and RMSE values (0.84, 1.10, 1.90, and 1.38) compared to the LSTM model. The critical contribution of this study lies in its development of comprehensive and effective methods for accurately predicting microsleep events from EEG signals. UTeM 2024 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/40814/1/Microsleep%20Predicting%20Comparison%20Between%20LSTM%20and%20ANN%20Based%20on%20the%20Analysis%20of%20Time%20Series%20EEG%20Signal.pdf Hasan, Md Mahmudul and Hossain, Mirza Mahfuj and Norizam, Sulaiman and Khandaker, Sayma (2024) Microsleep Predicting Comparison Between LSTM and ANN Based on the Analysis of Time Series EEG Signal. Journal of Telecommunication, Electronic and Computer Engineering, 16 (1). pp. 1-7. ISSN 2180-1843 (Print); 2289-8131 (Online). (Published) https://jtec.utem.edu.my/jtec/article/view/6322 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Hasan, Md Mahmudul Hossain, Mirza Mahfuj Norizam, Sulaiman Khandaker, Sayma Microsleep Predicting Comparison Between LSTM and ANN Based on the Analysis of Time Series EEG Signal |
| title | Microsleep Predicting Comparison Between LSTM and ANN Based on the Analysis of Time Series EEG Signal |
| title_full | Microsleep Predicting Comparison Between LSTM and ANN Based on the Analysis of Time Series EEG Signal |
| title_fullStr | Microsleep Predicting Comparison Between LSTM and ANN Based on the Analysis of Time Series EEG Signal |
| title_full_unstemmed | Microsleep Predicting Comparison Between LSTM and ANN Based on the Analysis of Time Series EEG Signal |
| title_short | Microsleep Predicting Comparison Between LSTM and ANN Based on the Analysis of Time Series EEG Signal |
| title_sort | microsleep predicting comparison between lstm and ann based on the analysis of time series eeg signal |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/40814/1/Microsleep%20Predicting%20Comparison%20Between%20LSTM%20and%20ANN%20Based%20on%20the%20Analysis%20of%20Time%20Series%20EEG%20Signal.pdf http://umpir.ump.edu.my/id/eprint/40814/ https://jtec.utem.edu.my/jtec/article/view/6322 |
| url_provider | http://umpir.ump.edu.my/ |