本論文提出一心電及活動感測器及其相關之生理狀態識別演算法。首先，本論文開發一基於心電訊號之心律不整類型辨識演算法，由心電訊號波形上擷取特徵，再透過主成分分析及線性識別分析等資料降維方法選取最具意義之特徵值並降低特徵空間之維度，並利用機率類神經網路成功地辨識心律不整類型。接著，本論文亦發展一個活動及心電訊號感測器及其睡眠階層分類演算法。利用睡眠時量測之身體活動及心電資料進行睡眠階層分類。為了提高辨識準確度，本論文使用一依序向前搜尋法尋找最具鑑別力之特徵值作為基於決策樹之支持向量機的輸入資料，該向量機用於分類四種睡眠階層，達到在居家環境中評估使用者的睡眠品質之目的。最後，本論文開發一情緒調節隨身聽系統及其音樂情緒和人類情緒感知演算法，用以客製化的判斷音樂是否觸發了使用者某一特定情緒。音樂情緒感知演算法由一基於核函數之類別可分離方法及無參數加權特徵萃取方法進行音樂訊號特徵的資料降維，再透過階層式支持向量機分類器進行音樂情緒感知；而人類情緒感知演算法則利用基因演算法選取最適合判別情緒變化的心電特徵，作為階層式支持向量機的輸入資料進行人類情緒感知。經由心律不整、睡眠分層及情緒感知的實驗結果可成功的驗證所提出的心電及活動感測器及其相關之生理狀態分析演算法之有效性。

This dissertation presents ECG and activity sensors and algorithms for its application to physiological condition recognition. First, an ECG arrhythmia classification algorithm is proposed. The algorithm extracts the waveform morphology features from ECG signals, and then further adopts feature reduction method, consisting of principal component analysis (PCA) and linear discriminant analysis (LDA), for selecting significant features. The reduced features are sent to a trained probabilistic neural network (PNN) for arrhythmia classification. Next, a wearable ECG-and-activity sensor system and its sleep stage recognition algorithm are proposed to classify sleep stages by using the combination of physical activity and ECG data. In order to improve the accuracy of the classifier, the sequential forward selection method is employed to find the significant features at each node of the decision-tree-based support vector machines (DTB-SVMs) classifier. The proposed classifier is then used to classify four types of sleep stage and evaluate at-home sleep quality. Finally, an emotion regulation music player system, consisting of music and human emotion detection algorithms, is proposed to identify the specific emotion induced by music and customize the music played for different people, based on their desired mood. The music emotion detection algorithm uses a kernel-based class separability (KBCS) feature selection method and a nonparametric weighted feature extraction (NWFE) method to reduce the dimensions of music features, and then a hierarchical SVMs classifier is utilized to detect the music emotion. The human emotion detection algorithm uses a genetic algorithm (GA) to select the fittest ECG features for a hierarchical SVMs classifier to detect the human emotion induced by music. The experimental results have successfully validated the effectiveness of the proposed ECG and activity sensors and algorithms for its application.

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