臺灣博碩士論文加值系統

由於具有快速及非侵入式之特性，心電圖(Electrocardiogram，ECG)是臨床醫師診斷心臟疾病的常用檢測方法之一。ECG波形圖中的P波、QRS波及T波，分別代表著心臟不同部位電生理的變化，臨床醫師可藉由這些波形的特徵，判斷不同的心臟疾病。例如QT節段延長症候群(Long QT syndrome)、急性心肌梗塞(Acute Myocardial Infarction，AMI)及心房顫動(Atrial Fibrillation，Af)等。一般來說，ECG所有的波形中，以QRS波最為明顯，找到QRS波之後，就能進一步找到P波及T波的位置。另一方面，若能準確的偵測到QRS波的位置，就能計算RR區間，進而做心率變異(Heart Rate Variability，HRV)的分析。近幾年來，許多的研究已提出不同的方法來偵測QRS波的位置，例如濾波器組(Filter banks)、人工智慧演算法、隱藏式馬可夫模型(Hidden Markov Models，HMM)、基因演算法及小波轉換等，其中小波轉換是最有前瞻性的方法。
本研究與苗栗為恭醫院合作，從院內收集近10000筆的臨床SCP-ECG資料，經過本實驗室所開發的SCP解碼程式轉成文字檔案，並將ECG內容及波形數據儲存於資料庫中。將這個資料庫與醫院之醫療資訊系統(HIS)結合，經由醫師確認後，本研究建立了正常人(Normal)及三種心臟疾病患者之ECG資料庫：急性心肌梗塞(AMI)、高血鉀(Hyperkalemia)及心房顫動(Af)等。為能進一步分析這些心臟疾病ECG之特性，本研究以MATLAB開發一個R波偵測程式，並提出一個新的複合式小波轉換法(小波包分析(wavelet packet analysis)及離散小波轉換(discrete wavelet transform，DWT))，來偵測QRS波的位置。研究結果顯示，此方法對上述正常人及三種心臟疾病之12導程ECG QRS波偵測的敏感度(sensitivity)依次為100%、99.51%、99.72%和99.65%，而正確率(positive predictive value)依次為100%、99.46%、99.66%、99.88%。
由以上的結果顯示，此R波偵測程式不管在ECG訊號的基線漂移或參雜有雜訊的狀況下亦能準確的偵測R波，且適用於ECG的任何一個導程及各式的心臟疾病。同時，此程式在單一導程的準確率也很高，未來亦可應用在Holter ECG上。

Electrocardiogram (ECG) is one of the most used tools for diagnosis of heart-related diseases due to its fast operational and noninvasive features. P wave, QRS complex, and T wave reflect the change of electrophysiological conditions of heart cells at various positions of heart. The clinician can diagnose different heart diseases basing on the characteristic of these wave forms, for example, Long QT syndrome, Acute Myocardial Infarction (AMI), and Atrial Fibrillation (Af), and so on. Generally, the R wave is the most prominent waveform within the ECG signal. After locating the R waves, the positions of P and T waves can found either. In addition, if the R waves can be located accurately, we can also calculate the R-R intervals, which then can be used for analysis of Heart Rate Variability (HRV). In recent years, many of researches have already put forward various methods to detect R wave, for example, filter banks, artificial intelligence algorithms, Hidden Markov Models (HMM), genetic algorithm and wavelet transform. Wavelet transform is the most promising method.

In this study we cooperate with Wei-Gong Memorial Hospital in Miao-Li County. About 10000 clinical SCP-ECG records have been collected from the emergency department of the Hospital. These records were decoded into text files through the SCP decoding program developed in our laboratory, and the ECG information and wave form data were stored within a database. Linked with the HIS and confirmed by clinical physicians, we have been able to construct disease-specific ECG Databases for AMI, Hyperkalemia, and Af, and so on. In order to analyze the features of ECG’s of these heart diseases, an R wave delineator was developed in MATLAB, which used a novel hybrid wavelet transform method (includes wavelet packet analysis and discrete wavelet transform (DWT)) to detect R waves. The results showed that the sensitivities of R wave detection were 100%, 99.51%, 99.72%, 99.65% for normal, AMI, Hyperkalemia, and Af ECG’s respectively; and the positive predictive value of R wave detection were 100%, 99.46%, 99.66%, 99.88% for previous four categories of ECG records.

As shown in the above results, the algorithms developed in this study can be applied directly to clinical 12-lead ECG records for waveform analyses with high accuracy of R wave detection in various leads and diseases, regardless of interferences embedded in an ECG record such as baseline wandering, muscle contraction noise, and patient movement. The R wave detecting tool can also be applied to Holter ECG systems for wave form analysis because of its robust ability for processing single-lead ECG signals.

中文摘要 i
abstract iii

論文目錄 vi
圖表目錄 viii

第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 1
第二章 文獻回顧 3
2.1 12導程心電圖 3
2.1.1 肢導程(limb leads) 4
2.1.2 胸導程(chest leads) 5
2.1.3 心電圖的波形 6
2.2 心臟疾病介紹 7
2.2.1 正常人 7
2.2.2 高血鉀症 7
2.2.3 急性心肌梗塞 8
2.2.4 心房顫動 10
2.3 心電圖特徵點的萃取之相關文獻 11
第三章 研究工具及方法 13
3.1 心電圖資料的取得 13
3.2 資料分析 14
3.2.1 小波(Wavelet)的歷史起源 14
3.2.2 小波包分析(Wavelet Packet Analysis) 15
3.2.3 R波偵測演算法 18
第四章 結果與討論 29
4.1 個案研究 29
4.1.1 心電圖中包含較高的T波 29
4.1.2 基線漂移 30
4.1.3 心電圖中參雜一些雜訊 31
4.1.4 心率變化不規律 33
4.1.5 偵測錯誤的例子 34
4.2 測試結果 35
第五章 結論與展望 36
5.1 結論 36
5.2 未來展望 36
參考文獻 38

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