本研究採用人體心電訊號作為身分辨識系統的訊號來源，選擇以智慧型行動裝置作為實現演算法與儲存資料的平台，並搭配自製的心電訊號量測裝置記錄人體心電訊號。訊號處理部分使用Hamilton R波偵測演算法，擷取出連續心電訊號中的單一心跳週期，透過拉普拉斯高斯處理將單一心跳週期轉換為獨特的特徵波形，再以此特徵波形作為使用者進行身分辨識的依據。準確度驗證以心電訊號資料庫中的資料進行測試，使用MIT-BIH NSRDB以及EDB進行驗證分別可以得到90.28%以及94.44%的準確率，而在實際使用硬體裝置量測5位受試者在不同時間間隔下的訊號時，可以成功的辨識個別受試者的身分。

This thesis presents a new approach of ECG Biometric method by using the intelligent device as the platform of human identification. Proposed system uses a custom hardware device to capture the ECG signal of users and the intelligent device to execute the feature extraction algorithm. In the phase of signal pre-processing, Hamilton method is applied to detect R peaks in continuous ECG signal and separates it into several single cardiac cycle. Through the feature extraction algorithm, single cardiac cycles captured from continuous ECG signal can be transformed into a particular feature waveform and is used for human identification. For verifying accuracy and usability of the proposed system, we use MIT-BIH NSRDB and EDB of PhysioBank ECG database to obtain signals recorded from different time interval then get the accuracy of 90.28% and 94.44%. In addition to database verification, proposed system also takes a clinical trial for 5 subjects and proposed system is successfully identifying all 5 subjects in this clinical trial.

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