A Takagi-Sugeno Fuzzy Neural Network-based Algorithm with Single-Channel EEG Signal for the Discrimination between Light and Deep Sleep Stage

睡眠品質一向是現代人們最重視的問題，當睡眠不足或睡不好時，會間接影響到日常生活步調和工作效率。為了改善這些問題，許多醫院成立睡眠品質診斷中心，透過專業的儀器與人力諮詢方式解決現在睡眠上的問題，但其服務背後的高精密儀器設施與人力花費都是相當高昂。然而，在睡眠品質診斷中心檢測時，睡眠記錄儀器必須貼上較多的電極，進行多種訊號的擷取，例如：腦波、心電圖、肌電圖、血氧飽和指數等，患者因身體或頭部貼滿許多電極，可能會造成患者不舒服，因而對患者在睡眠品質上有較大的干擾，使得人工判讀的結果無法真正反映出患者的真實睡眠情況且人工判讀結果往往較為耗時。 因此本論文提出「基於Takagi-Sugeno網路與單一腦波訊號之淺層與深層睡眠自動判讀演算法」，主要利用單一腦波訊號，搭配訊號處理與Takagi-Sugeno類神經網路的方法，進行淺層與深層睡眠的判別。利用單一腦波訊號分析淺層與深層睡眠的優點，相較於睡眠記錄儀器，只需使用兩個電極所擷取出來的腦波訊號，即可自動進行睡眠判讀，可減少患者的不舒服感，且能使患者能將真實睡眠情況反映出來，並且提高睡眠判別的準確度。People pay attention to their sleep quality and sleep problems. When people don''t have enough or qualified sleep, these conditions may have negative impacts on people''s life and their efficiency in work. In order to solve these problems, many hospitals set up sleep quality centers where professional instruments and consultations are used to solve sleep problems, but these advanced instruments and human efforts cost a lot. Besides, people must be stuck with many electrodes to collect signals, such as electroencephalogram (EEG), electrocardiogram (ECG), electromyography(EMG), oxygen desaturation index and so on, before people receive their diagnosis and analysis for their sleep conditions. During the examination, people with electrodes feel uncomfortable due to lots of electrodes influencing their sleep, so the results might be incorrect and unable to reflect the real conditions. To address these problems, we propose the Takagi-Sugeno fuzzy neural network-based algorithm with single-channel EEG signal for the discrimination between light and deep sleep stage. This main algorithm is using the single-channel EEG to combine signal processing and Takagi-Sugeno neural network to discriminate between light and deep sleep. The advantage of using the single-channel EEG is decreasing people''s uncomfortable feeling, reflecting the real sleep conditions, and increasing the accuracy by using two electrodes to get the EEG signal