近年來，睡眠呼吸中止症(Sleep Apnea Syndrome)是常見的睡眠疾病，其症狀為患者在睡眠中會出現頻繁的呼吸中止，進而引起大腦的缺氧，因而影響健康，嚴重的病患亦可能導致死亡。根據文獻，睡眠呼吸中止後腦波會發生頻帶的轉移現象，此現象稱為喚醒(Arousal)，可當作睡眠呼吸中止的腦波特徵。故在本論文中，我們首先分析分類器與喚醒之間的關係。然後，我們提出以基因演算法為基礎之方法進行睡眠腦波喚醒的混合式分類器偵測模型建置。所提的方法中，首先使用快速傅立葉轉換(Fast Fourier Transform)進行不同頻帶的腦波訊號擷取。接著，從所得的腦波訊號再進行特徵擷取後，經由所提的方法建置睡眠腦波喚醒偵測的混合式分類器模型。因此，在所提的方法中每條染色體表示ㄧ個可能的混合式分類器模型。而在評估函數(fitness function)部分，我們使用準確度(Precision)、涵蓋度(Recall)和F-measure三種評估函數進行染色體的適合度(fitness)的計算。最後，最好的染色體即為我們所得到的最佳混合式分類器模型。實驗部分，我們使用十六位患有睡眠呼吸中止症的腦波進行分析。首先，使用單一分類器和單一分類器整合效能提升演算法來分析喚醒與分類模型之間的關係。最後，針對所提的方法進行實驗的驗證，實驗結果證實由所提的方法不但可以得出適合的睡眠腦波喚醒偵測模型，而且在模型效能上亦有顯著的改善。

Sleep apnea is a common sleep disorder, the symptom is that patients may show respirating pause frequently during sleep such that cause brain injury due to lack of oxygen and result in poor health. A considerable portion of cases may even cause death. According to literatures, when sleep apnea syndrome happens, electroencephalogram (EEG) may show shifting phenomena on the frequency band, particularly inducing “Arousal”. Arousal can thus be considered as the features of sleep apnea on EEG. In this thesis, we first analyze the relationship between arousal and classification model. We then propose a GA(Genetic Algorithms)-based approach for building a hybrid classification model for arousal detection. In the proposed approach, the Fast Fourier transform (FFT) is first used to convert the EEG signal into frequency domain. Features are then extracted from these transformed data and used as classification attributes in the proposed approach. Thus, each chromosome represents a possible hybrid classification model for arousal detection. Three fitness functions, including Precision, Recall and F-measure, are used to evaluate the fitness of a chromosome. After the evolution processing, the best chromosome is then output as the final hybrid classification model. In the experiments, EEG signals of the sixteen patients that have sleep apnea syndrome are used for analyzing. First, the experiments were made to show the relationship among single classifier and single classifier with improved approach and arousal. The experiments were then made to verify the proposed approach and the results show that the proposed approach can not only derived appropriate hybrid classification model, but also has significant improvement on the accuracy of the classification model.

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