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研究生:魏士穎
研究生(外文):Wei, Shih-Ying
論文名稱:基於深度學習之可擴充開放式心電識別系統
論文名稱(外文):A Scalable Open-set ECG Identification System Based on Deep Learning
指導教授:吳順吉
指導教授(外文):Wu, Shun-Chi
口試委員:溫宏斌葉秩光
口試日期:2020-07-31
學位類別:碩士
校院名稱:國立清華大學
系所名稱:工程與系統科學系
學門:工程學門
學類:核子工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:40
中文關鍵詞:生物辨識心電圖深度學習遷移學習
外文關鍵詞:Biometric recognitionElectrocardiogramsDeep learningTransfer learning
相關次數:
  • 被引用被引用:1
  • 點閱點閱:330
  • 評分評分:
  • 下載下載:9
  • 收藏至我的研究室書目清單書目收藏:0
隨著科技發展,許多個性化的服務帶給人們更便利的生活,然而在享受這便利的同時,保障個人資訊也成為重要的議題。但傳統的身分識別方法(如:鑰匙或密碼)卻不夠嚴謹,不但容易被盜用,更有遺失的可能,因此運用生物特徵為依據的生物辨識技術受到許多關注,而心電訊號是其中一具前景的生物特徵。心電訊號是一紀錄心臟活動的訊號,相較於外顯的生物特徵(如:指紋、臉孔)較難以竊取;此外,心電訊號是一個動態的訊號,僅在個體存活時能夠被測量。雖然心電訊號存在許多好處,但「動態」的特性增加心跳的變異率,也增加辨識的難度。本研究提出一種基於深度學習的心電識別系統,並解決現存系統中的兩個問題。首先,卷積神經網路能透過存在系統內的特徵向量與遷移學習微調系統,使我們不須召回已註冊者就能讓新使用者能註冊於系統。其次,識別系統將未知對象的特徵向量與數據庫中保存的特徵向量進行比較,使用Parzen Window估計概率,並通過均值和標準差來區分未註冊的對象。最後,以PTB資料庫中的255受測者驗證資料庫的識別效能與安全性,在開放式的識別下達到IR為97.5%且FPIR為2.5%。
As technology advances, many personalized services provide a comfortable and humane service. While enjoying the convenience of technology, protecting personal information has also become an important issue. However, traditional identification methods (e.g., keys or passwords) are not strict enough, and not only are they easy to be stolen, but it may also be lost. Therefore, an identification system based on biometrics has attracted considerable attention and using electrocardiograms (ECGs) as biometric is a promising option. ECGs are recordings of the heart's electrical activity and are hard to be stolen compared to extrinsic biometrics (e.g., fingerprints, faces, etc.). Besides, the ECG signal is a dynamic signal that can only be measured while the individual is alive. In this study, we propose a DL-based ECG identification scheme and address two problems of the current biometric system. First, with the help of “transfer learning,” new subjects can enroll our system by retraining the model with feature vectors. Second, the identification scheme will compare the feature vector of an unknown subject that will be compared with those saved in the database to estimate the probability with Parzen window, and discriminate an unregistered subject with mean and standard deviation. Finally, we verify the proposed open-set identification system with the PTB database of 255 subjects, and it achieves an IR of 97.5% and an FPIR of 2.5%.
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 研究背景 2
1.3 研究架構 5
第二章 文獻回顧 6
2.1 基於模板之方法 6
2.2 基於深度學習之方法 7
第三章 可擴充開放式心電識別系統 10
3.1 心電訊號前處理 10
3.2 建置神經網路 14
3.3 新註冊者 17
3.4 未註冊者排除機制 18
第四章 實驗結果 21
4.1 心電訊號資料庫 21
4.2 評估程序與錯誤指標(error metrics) 22
4.3 預訓練網路 24
4.4封閉式的識別系統(closed set identification) 25
4.4.1訓練資料樣本數 27
4.4.2使用者數量 28
4.5 開放式的識別系統(open set identification) 29
4.5.1同資料庫分組攻擊 29
4.5.2模擬心電訊號的攻擊 33
4.5.3特徵向量的攻擊 34
4.6 網路參數與計算複雜度 35
第五章 總結 36
5.1 結論 36
5.2未來工作 36
參考文獻 37
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