Single Channel EEG for Near Real-Time Sleep Stage Detection

Sleep disorders are considered as one of the major human life issues in the recent years. Therefore, efficient and automated systems that can differentiate sleep stages and assist physicians/neurologists in the diagnosis and treatment of sleep-related disorders, are highly on demand. The present paper is devoted to developing an easy-to-implement sleep stage classification algorithm that works fast (near real-time) in a proficient way. The proposed algorithm is based on two statistical features applied to single-channel EEG signals. We examined the effectiveness of our technique by building a near real-time detection system using the Neurosky's Mindwave Mobile device, an affordable wireless EEG headset, to obtain EEG signals. The system is one-way without feedback loops. The results of analyzing our algorithm show that the run-time performance of this detection technique is quasi-linearly proportional to the size of the input samples and the execution time is fast, regardless of the time recording the data.