With the rising awareness of the critical role sleep plays in both health and social well-being, the demand for sleep studies is rapidly increasing.Automatic sleep stage classification is a fundamental part of sleep measurement, and machine learning models have been developed to assist in this process. These models achieve accuracy comparable to that of technicians when using data from healthy individuals. However, sleep patterns in individuals with sleep disorders, such as sleep apnea syndrome (SAS), one of the most common sleep disorders, differ from those of healthy individuals. As a result, existing models trained on healthy individuals’ data do not achieve sufficient accuracy when applied to SAS patients. This is a barrier to clinical application.A recent study using in-home EEG devices showed that technicians can accurately classify sleep stages in SAS cases by considering surrounding epochs. Based on this, we developed a model dedicated to SAS patients that incorporates the temporal context of relevant epochs.We found that this context-aware model significantly improved classification accuracy compared to models that only focused on the target epoch. In the training process using data from 76 severe SAS cases, the model based solely on single-epoch data achieved an accuracy of 71.5%, while the model considering the surrounding epochs achieved an accuracy of 73.7%. The classification accuracy improved across all stages except N3.This approach appears to capture the frequent sleep stage transitions characteristic of SAS.

We report an experiment on the relationship between sleep and cooperative behavior by using Electroencephalogram (EEG) measuring devices and Psychomotor Vigilance Test (PVT) in measuring subjects’ alertness. In this experiment, two types of games—prisoner’s dilemmas and coordination games—were conducted by controlling subjects’ sleep. We found that lower alertness measured by PVT entails more cooperation in both games. On the other hand, we did not observe a significant relationship between sleeping time and cooperation.

Given brain and myoelectric signals taken from a mouse, how can we classify its sleep stages accurately? Classifying sleep stages is the fundamental problem in recent diagnoses and clinical researches. However, sleep staging suffers from a serious weakness, clinical experts visually inspect the brain and myoelectric signals to improve sleep staging accuracy. This is because recent diagnoses and clinical researches require classification accuracy at least 95% so as to enhance preciseness of their analyses. In this paper, we present an automatic classification method MASC based on the following three approaches: (1) it extracts effective features for fully representing each sleep stage property, (2) it classifies sleep stages by using temporal patterns of sleep stage transitions, and (3) it re-classifies sleep stages only for the results with low-confidence. As a result, MASC achieves more than 95% accuracy for both noisy and noiseless mice data.