Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning
Poor-quality sleep substantially diminishes the overall quality of life. It has been shown that sleep arousal serves as a good indicator for scoring sleep quality. However, patients are conventionally asked to perform overnight polysomnography tests to collect their physiological data, which are use...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/18/6049 |
| _version_ | 1827681291407982592 |
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| author | Ying-Ren Chien Cheng-Hsuan Wu Hen-Wai Tsao |
| author_facet | Ying-Ren Chien Cheng-Hsuan Wu Hen-Wai Tsao |
| author_sort | Ying-Ren Chien |
| collection | DOAJ |
| description | Poor-quality sleep substantially diminishes the overall quality of life. It has been shown that sleep arousal serves as a good indicator for scoring sleep quality. However, patients are conventionally asked to perform overnight polysomnography tests to collect their physiological data, which are used for the manual judging of sleep arousals. Even worse, not only is this process time-consuming and cumbersome, the judgment of sleep-arousal events is subjective and differs widely from expert to expert. Therefore, this work focuses on designing an automatic sleep-arousal detector that necessitates only a single-lead electroencephalogram signal. Based on the stacking ensemble learning framework, the automatic sleep-arousal detector adopts a meta-classifier that stacks four sub-models: one-dimensional convolutional neural networks, recurrent neural networks, merged convolutional and recurrent networks, and random forest classifiers. This meta-classifier exploits both advantages from deep learning networks and conventional machine learning algorithms to enhance its performance. The embedded information for discriminating the sleep-arousals is extracted from waveform sequences, spectrum characteristics, and expert-defined statistics in single-lead EEG signals. Its effectiveness is evaluated using an open-accessed database, which comprises polysomnograms of 994 individuals, provided by PhysioNet. The improvement of the stacking ensemble learning over a single sub-model was up to 9.29%, 7.79%, 11.03%, 8.61% and 9.04%, respectively, in terms of specificity, sensitivity, precision, accuracy, and area under the receiver operating characteristic curve. |
| first_indexed | 2024-03-10T07:14:05Z |
| format | Article |
| id | doaj.art-10def155c19847539f709937638fd4eb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T07:14:05Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-10def155c19847539f709937638fd4eb2023-11-22T15:10:49ZengMDPI AGSensors1424-82202021-09-012118604910.3390/s21186049Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble LearningYing-Ren Chien0Cheng-Hsuan Wu1Hen-Wai Tsao2Department of Electrical Engineering, National Ilan University, Yilan 26047, TaiwanGraduate Institute of Electronics Engineering, College of Electrical Engineering and Computer Science, National Taiwan University, Taipei 10617, TaiwanGraduate Institute of Communication Engineering, College of Electrical Engineering and Computer Science, National Taiwan University, Taipei 10617, TaiwanPoor-quality sleep substantially diminishes the overall quality of life. It has been shown that sleep arousal serves as a good indicator for scoring sleep quality. However, patients are conventionally asked to perform overnight polysomnography tests to collect their physiological data, which are used for the manual judging of sleep arousals. Even worse, not only is this process time-consuming and cumbersome, the judgment of sleep-arousal events is subjective and differs widely from expert to expert. Therefore, this work focuses on designing an automatic sleep-arousal detector that necessitates only a single-lead electroencephalogram signal. Based on the stacking ensemble learning framework, the automatic sleep-arousal detector adopts a meta-classifier that stacks four sub-models: one-dimensional convolutional neural networks, recurrent neural networks, merged convolutional and recurrent networks, and random forest classifiers. This meta-classifier exploits both advantages from deep learning networks and conventional machine learning algorithms to enhance its performance. The embedded information for discriminating the sleep-arousals is extracted from waveform sequences, spectrum characteristics, and expert-defined statistics in single-lead EEG signals. Its effectiveness is evaluated using an open-accessed database, which comprises polysomnograms of 994 individuals, provided by PhysioNet. The improvement of the stacking ensemble learning over a single sub-model was up to 9.29%, 7.79%, 11.03%, 8.61% and 9.04%, respectively, in terms of specificity, sensitivity, precision, accuracy, and area under the receiver operating characteristic curve.https://www.mdpi.com/1424-8220/21/18/6049arousalconvolutional neural network (CNN)ensemble learningelectroencephalography (EEG)meta-classifierpolysomnography (PSG) |
| spellingShingle | Ying-Ren Chien Cheng-Hsuan Wu Hen-Wai Tsao Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning Sensors arousal convolutional neural network (CNN) ensemble learning electroencephalography (EEG) meta-classifier polysomnography (PSG) |
| title | Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning |
| title_full | Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning |
| title_fullStr | Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning |
| title_full_unstemmed | Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning |
| title_short | Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning |
| title_sort | automatic sleep arousal detection with single lead eeg using stacking ensemble learning |
| topic | arousal convolutional neural network (CNN) ensemble learning electroencephalography (EEG) meta-classifier polysomnography (PSG) |
| url | https://www.mdpi.com/1424-8220/21/18/6049 |
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