Automatic Sleep Staging Algorithm Based on Time Attention Mechanism

Abstract

The most important part of sleep quality assessment is the automatic classification of sleep stages. Sleep staging is helpful in the diagnosis of sleep-related diseases. This study proposes an automatic sleep staging algorithm based on the time attention mechanism. Time-frequency and non-linear features are extracted from the physiological signals of six channels and then normalized. The time attention mechanism combined with the two-way bi-directional gated recurrent unit (GRU) was used to reduce computing resources and time costs, and the conditional random field (CRF) was used to obtain information between tags. After five-fold cross-validation on the Sleep-EDF dataset, the values of accuracy, WF1, and Kappa were 0.9218, 0.9177, and 0.8751, respectively. After five-fold cross-validation on the our own dataset, the values of accuracy, WF1, and Kappa were 0.9006, 0.8991, and 0.8664, respectively, which is better than the result of the latest algorithm. In the study of sleep staging, the recognition rate of the N1 stage was low, and the imbalance has always been a problem. Therefore, this study introduces a type of balancing strategy. By adopting the proposed strategy, SEN-N1 and ACC of 0.7 and 0.86, respectively, can be achieved. The experimental results show that compared to the latest method, the proposed model can achieve significantly better performance and significantly improve the recognition rate of the N1 period. The performance comparison of different channels shows that even when the EEG channel was not used, considerable accuracy can be obtained.

An Interpretable Attention-based Method for Gaze Estimation Using Electroencephalography

“…Attention mechanisms have recently emerged as a powerful tool for processing sequential data, including time-series data in various fields such as natural language processing, speech recognition, and computer vision [5,24,29]. In the context of EEG signal analysis, attention mechanism has shown promising results in various applications, including sleep stage classification, seizure detection, and event-related potential analysis [8,13,17]. Since different electrodes record the brain activity from the different brain areas and functions, the information density from each electrode can vary for different tasks [15].…”

Multi-scale ResNet and BiGRU automatic sleep staging based on attention mechanism

“…The accuracy of sleep staging has been greatly improved. Feng et al [ 26 ] proposed an automatic sleep staging algorithm based on the time attention mechanism. This approach reduces computing resources and time costs.…”