https://scholars.lib.ntu.edu.tw/handle/123456789/607090
標題: | Automatic sleep-arousal detection with single-lead eeg using stacking ensemble learning | 作者: | Chien Y.-R Wu C.-H Tsao H.-W. HEN-WAI TSAO |
關鍵字: | Arousal;Convolutional neural network (CNN);Electroencephalogra-phy (EEG);Ensemble learning;Meta-classifier;Polysomnography (PSG);Recurrent neural network (RNN);Biomedical signal processing;Chemical detection;Convolution;Convolutional neural networks;Decision trees;Electroencephalography;Learning systems;Sleep research;Conventional machines;Electroencephalogram signals;Embedded information;Physiological data;Random forest classifier;Receiver operating characteristic curves;Recurrent networks;Spectrum characteristic;Recurrent neural networks;arousal;electroencephalography;human;machine learning;quality of life;sleep;sleep stage;Humans;Machine Learning;Quality of Life;Sleep;Sleep Stages | 公開日期: | 2021 | 卷: | 21 | 期: | 18 | 來源出版物: | Sensors | 摘要: | 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 con-ventionally 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 algo-rithms 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. ? 2021 by the authors. Licensee MDPI, Basel, Switzerland. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114554586&doi=10.3390%2fs21186049&partnerID=40&md5=8ea5e4eb49913c0c2051c2b023faba39 https://scholars.lib.ntu.edu.tw/handle/123456789/607090 |
ISSN: | 14248220 | DOI: | 10.3390/s21186049 |
顯示於: | 電機工程學系 |
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