Automatic Sleep Scoring Using Intrinsic Mode Based on Interpretable Deep Neural Networks

Abstract

Sleep experts manually label sleep stages via polysomnography (PSG) to diagnose sleep disorders. However, this process is time-consuming, requires a lot of labor from sleep experts, and makes the participants uncomfortable with the attachment of multiple sensors. Thus, automatic sleep scoring methods are essential for practical sleep monitoring in our daily lives. In this study, we propose an automatic sleep scoring model based on intrinsic oscillations in a single channel electroencephalogram (EEG) signal. We applied noise assisted bivariate empirical mode decomposition (NA-BEMD) to extract the intrinsic mode components and an attention mechanism in deep neural networks to provide weights to the components depending on their significance to sleep scoring. In particular, through the attention mechanism, we found an interpretable model by examining the oscillations that correspond to specific sleep stages. Therefore, we analyzed which frequency components are more weighted to a sleep stage than the others, when the model classifies sleep stages, and, as a result, confirmed that the model assigns convincing weights to the frequency components for each sleep stage. Additionally, the model consists of a one-dimensional convolutional neural network (1D-CNN) to extract features of an epoch and bidirectional long short-term memory (Bi-LSTM) to learn the sequential information of the consecutive epochs. We evaluated proposed model using Fpz-Cz, Pz-Oz, and F3-M2 channel EEG from three different public datasets (Sleep-EDF-2013, Sleep-EDF-2018, WSC) and demonstrated that our model yielded the best overall accuracy (Fpz-Cz: 86.22%-82.67%, Pz-Oz: 83.63%-80.15%, F3-M2: 84.20%) and macro F1-score (Fpz-Cz: 80.79%-76.90%, Pz-Oz: 76.89%-72.98%, F3-M2: 74.88%) compared with the state-of-the-art sleep scoring algorithms using single channel EEG. As a benchmark test, FIR bandpass filters were compared, and it was confirmed that NA-BEMD was superior to the traditional filters in all experiments, demonstrating that the proposed model is interpretable and a state-of-the-art sleep scoring algorithm.

Keywords

• Electroencephalogram (EEG)
• automatic sleep scoring
• deep neural networks
• attention mechanism
• bivariate empirical mode decomposition