心臟因去極化及再極化而有收縮與舒張的現象，過程中所產生之電位訊號即為心電訊號（ECGs）。因個體心肌結構略有不同，所以每個個體間能產生不同的心電訊號，使得心電訊號可以作為生物辨識用之生物特徵。相較於其他生物特徵（如：指紋、臉孔），因心電訊號為體內產生之生理訊號，所以難以被竊取或盜用。本研究提出一個基於單導程心電訊號之生物辨識演算法，並將此演算法應用在身份識別模式中。我們提出的子空間過度取樣模板建置方法，能夠將心電訊號轉換為多樣化且不可逆之模板，以避免交叉比對的問題。透過子空間匹配的概念，我們不須提供任何隨機投影矩陣，即可進行心跳與模板之間的比對。最後，我們也提出了一個未註冊者的排除機制，以避免未註冊於資料庫中的登入者錯誤地被連結至任一已註冊者，此舉能夠大幅的增強系統的安全性。針對本研究提出之生物辨識演算法，我們將用300人的心電訊號資料庫驗證其辨識效能及安全性。

Electrocardiograms (ECGs) describe the electrical activity of the heart by electrodes placed on the skin. These electrodes detect small electrical changes which are caused by cardiac cells depolarization and depolarization during each cardiac cycle. Because muscular structures in myocardia between individuals are different, individuals can generate dissimilar ECGs, that makes ECGs a biometric modality for identity recognition. Compared with other extrinsic biometrics (such as fingerprints and faces), ECGs are difficult to steal or counterfeit. In this research, an identity recognition scheme based on single-lead ECG is proposed and applied on identification mode. To avoid the problem of cross-matching and privacy invasion, we propose a template construction to convert ECGs into diversified and irreversible templates through the concept of “subspace oversampling.” By the method of “subspace matching,” we can determine the identity of unknown subjects only with his/her beat bundles and templates in the database. Therefore, the information of template construction is not needed during identification. An exclusion of unregistered subjects is also implemented to prevent registered identity from being incorrectly linked by unregistered subjects, which can greatly enhance system security. Finally, an ECG database of 300 subjects is used to verify the identification performance and the security of the proposed scheme.

摘要 i
Abstract ii
致謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1 研究緣起 1
1.2 論文架構 2
第二章 現有心電辨識演算法回顧 3
2.1 心電訊號簡介 3
2.2 心電辨識系統簡介 3
2.3 可刪式生物辨識系統簡介 6
第三章 基於單導程心電訊號之生物辨識演算法 9
3.1 Beat bundle的訊號子空間 9
3.2 生物識別模板建置 9
3.3 識別用戶身分 11
3.4 未註冊者排除機制 12
3.5 系統流程 14
第四章 實驗結果 16
4.1 心電訊號資料庫及預處理 16
4.2 識別系統效能驗證 19
4.2.1 心跳的持續時間 19
4.2.2 取樣頻率 21
4.2.3 註冊/識別心跳平均數量 22
4.3 多樣性及不可逆性 24
4.4 安全性 30
4.5 計算複雜度 36
第五章 結論 38
參考文獻 39

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