A Review of Methods for Sleep Arousal Detection Using Polysomnographic Signals

Abstract

Multiple types of sleep arousal account for a large proportion of the causes of sleep disorders. The detection of sleep arousals is very important for diagnosing sleep disorders and reducing the risk of further complications including heart disease and cognitive impairment. Sleep arousal scoring is manually completed by sleep experts by checking the recordings of several periods of sleep polysomnography (PSG), which is a time-consuming and tedious work. Therefore, the development of efficient, fast, and reliable automatic sleep arousal detection system from PSG may provide powerful help for clinicians. This paper reviews the automatic arousal detection methods in recent years, which are based on statistical rules and deep learning methods. For statistical detection methods, three important processes are typically involved, including preprocessing, feature extraction and classifier selection. For deep learning methods, different models are discussed by now, including convolution neural network (CNN), recurrent neural network (RNN), long-term and short-term memory neural network (LSTM), residual neural network (ResNet), and the combinations of these neural networks. The prediction results of these neural network models are close to the judgments of human experts, and these methods have shown robust generalization capabilities on different data sets. Therefore, we conclude that the deep neural network will be the main research method of automatic arousal detection in the future.

Keywords: deep learning; machine learning; polysomnography (PSG); sleep arousal.

Figure 1

EEG channel diagram during arousal event.

Figure 2

Airflow, chest, and ABD channels during awakening events.

Figure 3

Number of publications papers which met the inclusion criteria per year.

Figure 4

Percentage distribution of different databases.

Figure 5

General workflow of sleep arousal detection models with machine learning.