Sleep Stage Classification with CNN-Transformer-combined Structure Using Single-Channel Raw ECG

Sleep disorders have been increasingly prevalent, and the necessity for sleep stage classification, a pivotal component in the diagnosis of sleep disorders, is also rising. The majority of sleep stage classifications employ multichannel biosignals derived from polysomnography. However, this approach is impractical, prompting the exploration of sleep stage models that utilize a single channel. In this study, we propose a model that automatically classifies sleep stages using a single ECG channel that is user-friendly and easily wearable. The proposed method integrates convolutional neural networks and transformer structures to learn both local and global information for sleep stage classification. In the context of 4-stage sleep stage classification (i.e., wake, light sleep, deep sleep, and rapid eye movement), the method attained accuracies of 76.12% and 63.42% on ISRUC-1 and SHHS-1, respectively, thereby demonstrating superior performance in comparison to baseline models. The proposed framework may offer significant potential for automatic sleep stage classification and may aid in the accurate diagnosis of sleep disorders.Clinical relevance - This is straightforward and can be utilized to diagnose sleep disorders, such as sleep apnea, by enhancing the precision of sleep stage classification.