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研究生:張正宏
研究生(外文):Cheng-hung Chang
論文名稱:基於智慧型裝置的心電訊號身分辨識系統
論文名稱(外文):An Intelligent Device Based Human Identification System Using ECG Signal
指導教授:林淵翔
指導教授(外文):Yuan-hsiang Lin
口試委員:林淵翔
口試委員(外文):Yuan-hsiang Lin
口試日期:2014-07-28
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:電子工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:中文
論文頁數:54
中文關鍵詞:心電訊號身分辨識訊號處理智慧型行動裝置
外文關鍵詞:ECGBiometricSignal ProcessingIntelligent Device
相關次數:
  • 被引用被引用:1
  • 點閱點閱:242
  • 評分評分:
  • 下載下載:15
  • 收藏至我的研究室書目清單書目收藏:0
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.
中文摘要 I
Abstract II
誌謝 III
目錄 IV
表目錄索引 V
圖目錄索引 VI
第一章 緒論 1
1.1 研究動機 1
1.2 文獻探討 3
1.3 研究目的 5
1.4 論文架構 6
第二章 研究背景 7
2.1 心電圖(Electrocardiogram, ECG) 7
2.1.1 人體心臟構造 7
2.1.2 心電訊號 9
2.1.3 心電訊號基本特徵 11
2.2 智慧型行動裝置 12
2.3 藍牙無線傳輸技術 13
第三章 研究方法 15
3.1 系統架構 15
3.2 心電訊號擷取 16
3.3 心電訊號處理 17
3.3.1 R波偵測演算法 17
3.3.2 單一心跳週期擷取 22
3.4 特徵波形轉換 24
3.5 身分辨識 31
3.6 智慧型行動裝置應用程式 32
第四章 實驗結果 36
4.1 心電訊號R波偵測結果 36
4.2 身分辨識準確度驗證 37
4.2.1 應用心電訊號資料庫之準確度驗證 37
4.2.2 即時人體心電訊號之準確度驗證 48
4.3 準確率比較 49
第五章 結論與未來展望 51
參考文獻 52
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