本研究採用心電圖訊號做為身分辨識的訊號來源，結合特定的訊號前處理方法，以Pan-Tompkins演算法快速且準確的偵測R波位置，進而分割心電圖訊號成為單一心跳週期訊號。利用此單一心跳週期訊號，與基於心電圖動態模型之數學模式所產生的模擬心電圖訊號擬合，透過使用演化式計算技術之PSO粒子群最佳化演算法萃取對應心電圖的動態模型參數。以此動態模型參數做為身分辨識的參考依據，並利用倒傳遞類神經網路建構辨識的部分。經初步實驗測試的結果，身份辨識準確率可達94.5%。

The thesis presents a new approach for ECG feature extraction and personal biometric identification from the recorded ECG profiles obtained from MIT-BIH database. We implement an algorithm based on specific signal processing methods, electrocardiogram dynamical model (EDM), and particle swarm optimization algorithm (PSO) for ECG feature extraction. In the signal processing phase, the major procedures apply Pan-Tompkins method for R peak detection and ECG cycle-to-cycle separation. Besides, the specific pre-processing procedures are also included to improve the overall performance when combining with EDM and PSO. In the feature extraction phase, we use one cycle ECG data after pre-process, ECG profile is performed to be synthesizable based on EDM parameters to mathematically modeling ECG morphological features which can be extracted by means of the PSO algorithm. The thesis includes the implementation details and discussions on the overall algorithm. And regarding to the preliminary verification results, a supervised learning algorithm. And the back propagation neural network (BPN) are applied for training, classification and recognition method and it shows that the system can provide accuracy rate of 94.5%.

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