本研究建立出一套基於 Android 智慧型裝置之心肌缺血偵測系統，在心臟病專科醫師診斷之前，使用者能利用輕便可攜帶的心電訊號感測器，連接手機後透過量測出的心電圖波型特徵，進行初步的檢查，若有缺血事件發生則提醒使用者就醫。本系統分為資料擷取、資料前置處理、 R 波偵測、特徵擷取以及心肌缺血判讀。由於缺乏罹患心肌缺血相關疾病之受測者心電訊號，本研究之心電訊號擷取自 PhysioNet 之 Long Term ST database (LTSTDB) 資料庫提供的心電訊號，於 MATLAB 實作帶通濾波器，消除基線飄移之影響後，將訊號存入 Android 裝置，藉由 Android 裝置讀取訊號並分析。R 波偵測方面，利用滑動視窗 (Sliding Window) 找出視窗中用來判斷 R 波之參考，找出 R 波。並擷取每個 R 波對應的 Q 波、 S 波、基準線高度、 ST 段以及 T 波等特徵，透過每個 R 波對應的 ST 段，以及 T 波之特徵，判定是否為心肌缺血事件，並將所有事件分析結果顯示於 Android 裝置上。

In this thesis, we proposed a myocardial ischemia detection system for Android-based smartphones. Before the cardiologist make a diagnosis, the users can connect the portable ECG sensor to the Android device and use the morphological features of the electrocardiography (ECG) signals to make an initial checkup. If the myocardial ischemic event occurs, the system will warn the user to seek medical attention. This system includes data acquisition, data pre-processing, R wave detection, features extraction and myocardial ischemia detection. Because our lack of the ECG signals from the subject that has been diagnosed with myocardial ischemia related heart disease, we acquires the ECG signals from the Long Term ST Database, which is included in PhysioNet website. The system uses the band-pass filter on MATLAB platform to remove the effect of baseline wander on ECG signals, then transmitting the signals into Android device and using the device to load and analyze the signals. R wave detection method is based on the sliding window to find the reference, which generates threshold to find R wave. Features extraction aims at finding the Q wave, S wave, baseline, ST segment and T wave responding to each R wave. Finally, using the features of ST segment and T wave to judge whether it's ichemic events or not. Finally, the system collects the information of the events and displays the detection results on the Android device.

致謝i
中文摘要ii
Abstract iii
List of Figures vi
List of Tables ix
1 緒論1
1.1 前言. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 研究動機與目的. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 研究方法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.4 論文架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2 研究背景3
2.1 心電圖簡介. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.1.1 心電圖基本原理. . . . . . . . . . . . . . . . . . . . . . . . . 3
2.1.2 心臟傳導系統. . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1.3 心電圖量測. . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.4 心電訊號雜訊. . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.1.5 心電圖波型. . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1.6 心肌缺血於心電圖表現. . . . . . . . . . . . . . . . . . . . . 14
2.1.7 心肌梗塞心電圖表現. . . . . . . . . . . . . . . . . . . . . . 18
iv
2.1.8 容易與心肌缺血混淆的情況. . . . . . . . . . . . . . . . . . 23
2.2 Android平台. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.3 PhysioNet介紹. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.4 相關研究與文獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.4.1 一個即時的QRS偵測演算法. . . . . . . . . . . . . . . . . . 35
2.4.2 短暫區間之心電訊號實現特徵擷取與多種心臟疾病之檢測. 36
3 系統架構與實作39
3.1 系統整體架構介紹. . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.1.1 演算法架構. . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.2 Android介面架構與實作. . . . . . . . . . . . . . . . . . . . . . . . 62
3.2.1 Android介面架構. . . . . . . . . . . . . . . . . . . . . . . . 62
3.2.2 Android應用程式執行結果. . . . . . . . . . . . . . . . . . . 63
4 測試結果68
5 未來展望76
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