Abstract
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.
Highlights
Poor-quality sleep negatively affects work performance [1] as well as emotional states [2,3]
Note that we proposed using a pre-merge module to combine the information at the input of the dense layer in both 1D convolutional neural network (CNN) and recurrent neural network (RNN) networks
The resulting performances associated with the CNN and RNN sub-models were below 80%, the meta-classifier could exploit the advantages of each sub-model to boost the overall performance
Summary
Poor-quality sleep negatively affects work performance [1] as well as emotional states [2,3]. A common measure of poor-quality sleep is sleep arousals [4]. “An abrupt shift in electroencephalogram frequency, including alpha, theta, and/or frequencies greater than 16 Hz, lasting at least 3 s and with at least 10 s of previous stable sleep.”. Polysomnography (PSG) monitors a subject’s body functions during sleep, including brain activity as measured by electroencephalogram (EEG), eye movements as measured by electrocardiography (EOG), muscle activity or skeletal muscle activation as measured by electromyography (EMG), heart rhythm as measured by electrocardiogram (ECG), respiration flow, patient movements, and arterial oxyhemoglobin saturation (SaO2). Training set records are annotated with a patient’s sleep stages over time and any arousals experienced.
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