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Article

Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning

1
Department of Electrical Engineering, National Ilan University, Yilan 26047, Taiwan
2
Graduate Institute of Electronics Engineering, College of Electrical Engineering and Computer Science, National Taiwan University, Taipei 10617, Taiwan
3
Graduate Institute of Communication Engineering, College of Electrical Engineering and Computer Science, National Taiwan University, Taipei 10617, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Gwanggil Jeon
Sensors 2021, 21(18), 6049; https://doi.org/10.3390/s21186049
Received: 21 July 2021 / Revised: 24 August 2021 / Accepted: 6 September 2021 / Published: 9 September 2021
(This article belongs to the Special Issue Signal Processing and Machine Learning for Smart Sensing Applications)
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. View Full-Text
Keywords: arousal; convolutional neural network (CNN); ensemble learning; electroencephalography (EEG); meta-classifier; polysomnography (PSG); recurrent neural network (RNN) arousal; convolutional neural network (CNN); ensemble learning; electroencephalography (EEG); meta-classifier; polysomnography (PSG); recurrent neural network (RNN)
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MDPI and ACS Style

Chien, Y.-R.; Wu, C.-H.; Tsao, H.-W. Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning. Sensors 2021, 21, 6049. https://doi.org/10.3390/s21186049

AMA Style

Chien Y-R, Wu C-H, Tsao H-W. Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning. Sensors. 2021; 21(18):6049. https://doi.org/10.3390/s21186049

Chicago/Turabian Style

Chien, Ying-Ren, Cheng-Hsuan Wu, and Hen-Wai Tsao. 2021. "Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning" Sensors 21, no. 18: 6049. https://doi.org/10.3390/s21186049

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