Abstract
Sleep stage classification is an essential process of diagnosing sleep disorders and related diseases. Automatic sleep stage classification using machine learning has been widely studied due to its higher efficiency compared with manual scoring. Typically, a few polysomnography data are selected as input signals, and human experts label the corresponding sleep stages manually. However, the manual process includes human error and inconsistency in the scoring and stage classification. Here, we present a convolutional neural network (CNN)-based classification method that offers highly accurate, automatic sleep stage detection, validated by a public dataset and new data measured by wearable nanomembrane dry electrodes. First, our study makes a training and validation model using a public dataset with two brain signal and two eye signal channels. Then, we validate this model with a new dataset measured by a set of nanomembrane electrodes. The result of the automatic sleep stage classification shows that our CNN model with multi-taper spectrogram pre-processing achieved 88.85% training accuracy on the validation dataset and 81.52% prediction accuracy on our laboratory dataset. These results validate the reliability of our classification method on the standard polysomnography dataset and the transferability of our CNN model for other datasets measured with the wearable electrodes.
Highlights
IntroductionAn accurate sleep stage classification [1–3] plays a significant role in sleep quality monitoring and the diagnosis of disorders
Standard polysomnography includes the measurement of various physiological signals such as an electroencephalogram (EEG), an electrooculogram (EOG), an electromyogram (EMG), and an electrocardiogram (ECG)
This study shows the human-level performance of our convolutional neural network (CNN)-based sleep classification model in scoring the standard PSG dataset and presents the potential of its effective transferability to other types of datasets, such as our own custom lab dataset with novel nanomembrane electrodes
Summary
An accurate sleep stage classification [1–3] plays a significant role in sleep quality monitoring and the diagnosis of disorders. The polysomnogram (PSG) is widely used in the diagnosis of obstructive sleep apnea (OSA) syndrome [4]. PSG is non-invasive and consists of a simultaneous recording of multiple physiological parameters related to sleep and sleep disorders. A series of polysomnographic signals for a 30-second-long epoch is labeled as a certain sleep stage by an expert sleep scorer. Compared to the manual scoring of sleep stages, automatic sleep stage classification serves as a more efficient way to evaluate a large amount of sleep data. Machine learning algorithms have been adopted in automatic sleep stage classification to increase classification efficiency and performance in recent years. Conventional statistical machine learning algorithms, such as Support Vector Machine [1,5], Hidden Markov
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