EEG-CLNet: Collaborative Learning for Simultaneous Measurement of Sleep Stages and OSA Events Based on Single EEG Signal

Sleep-stage and apnea–hypopnea index (AHI) are the most important metrics in the diagnosis of sleep syndrome disease. In previous studies, these two tasks are usually implemented separately, which is both time- and resource-consuming. In this work, we propose a novel single electroencephalogram (EEG)-based collaborative learning network (EEG-CLNet) for simultaneous sleep staging and obstructive sleep apnea (OSA) event detection through multitask collaborative learning. The EEG-CLNet regards different tasks as a common unit to extract features from intragroups via both local parameter sharing and cross-task knowledge distillation (CTKD), rather than just sharing parameters or shortening the distance between different tasks. Our approach has been validated on two datasets with the same or better performance than other methods. The experimental results show that our method achieves a performance gain of 1%–5% compared with the baseline. Compared to previous works where two or even more models were required to perform sleep staging and OSA event detection, the EEG-CLNet could reduce the total number of model parameters and facilitate the model to mine the hidden relationships between different task semantic information. More importantly, it effectively alleviates the task bias problem in hard parameter sharing. As a consequence, this approach has notable potential to be a solution for a lightweight wearable sleep monitoring system in the future.

I. Introduction

The study of object evaluation on sleep syndrome disease has received increasing attention from researchers. In clinical diagnosis, sleep syndrome disease is usually monitored by polysomnography (PSG) composed of electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), electrocardiogram (ECG), airflow (AF), oxygen saturation (SaO2), and so on. Sleep staging and obstructive sleep apnea (OSA) event detection are the most important tasks in the procedure of judging patients’ sleep quality. According to the American Academy of Sleep Medicine (AASM) [1] rule, physicians usually identify sleep stages according to EEG, EOG, and EMG. OSA events, detected by blood oxygen and AF, are generally applied to somnipathy diagnosis which is a common disease and a concomitant symptom of some diseases. Furthermore, OSA events may cause a series of sleep problems and take side effects on the normal sleep architecture. For instance, patients with OSA-hypopnea syndrome can abruptly wake up during sleeping, accompanied by gasping or choking. Therefore, sleep structures are quite different among the OSA patients [2], [3], [4].