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研究生： 王奕能
Yi-Neng - Wang
論文名稱： 心電圖的R波波峰偵測方法之研究
A Study of ECG R-peak Detection Methods
指導教授： 林淵翔
Yuan-Hsiang Lin
口試委員: 陳維美
Wei-Mei Chen
林昌鴻
Chang Hong Lin
學位類別： 碩士
Master
系所名稱： 電資學院 - 電子工程系
Department of Electronic and Computer Engineering
論文出版年： 2017
畢業學年度： 105
語文別： 中文
論文頁數： 108
中文關鍵詞： 心電圖 、R波 、偵測
外文關鍵詞： R-peak, Pan-Tompkins, Hamilton-Tompkins
相關次數： 點閱：453  下載：0 
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    • 在心律偵測演算法中比較普遍的有Pan-Tompkins在1985年發表的演算法，但是其濾波頻帶較窄而沒有保留足夠的QRS complex的頻率成分、其非線性轉換對振幅的變化適應性差，而且閥值的更新方式會受到突波的影響，以及其searchback機制偵測不到二連律（bigeminy）和三連律（trigeminy）。目前網路上可以取得的開源R波偵測演算法只有PhysioNet的WFDB（waveform database）與EP Limited公司的Hamilton先生維護的OSEA（open source ECG analysis），所以能夠拿來比較的演算法不多。由於OSEA被比較多文獻引用，一方面OSEA也與上述的Pan-Tompkins有相同的問題，並且濾波器消除60Hz的能力比Pan-Tompkins差，因此本研究從驗證OSEA開始，再改進OSEA的濾波器以及訊號與雜訊的分水嶺，並進行R波偵測相關演算法的整合。
    本研究使用的測試資料為普遍可取得的而且具有醫學研究價值的MIT-BIH Arrhythmia Database（MITDB）。在R波的偵測受到諸如電源線頻率、肌電訊號、移動雜訊（motion artifact）、P波以及T波的影響下，本研究嘗試不同的濾波頻帶和參數以達最低的錯誤率。去除雜訊的部分使用二階的CIC（cascaded Integrator and Comb）濾波器建構帶通濾波器，再使用曲線擬合近似斜率，最後以energy calculation將訊號變成單一極性的波封訊號（envelope）作為偵測之用。偵測的部分採用時間遮沒（blanking）、閥值以及斜率來分別訊號與雜訊。
    OSEA經過本研究將參數調整之後有較低的錯誤率，實際應用時仍需視訊號的特徵調整參數（例如閥值之更新係數與移動平均的長度）。在相關演算法的整合中發現，能量的計算若以平方搭配移動平均（moving average）會得到較多的FN（false negative），但是個別記錄的錯誤率相差比較不大；若以Shannon energy則會降低FN，同時也增加FP（false positive）和錯誤率的差異度，因此演算法的應用視實際訊號的特徵而選用。相較於OSEA，本研究的濾波器能降低FP，若再搭配本研究的判別架構則能大大地降低錯誤率。

    Among all heart rate detection algorithms, the one developed by Pan-Tompkins is the most commonly used. However, its filtering system can not reserve enough content of QRS complex, its searchback mechanism can not detect bigeminy and trigeminy, and its non-linear transform can not adapt sudden changes in amplitude very well. There are only two open source R-peak detection algorithms that can be obtained from the Internet, i.e. WFDB （waveform database）of PhysioNet and OSEA（open source ECG analysis）of Hamilton at EP Limited corporation. OSEA was cited by more papers than WFDB was; besides, it has the same problems as those that Pan-Tompkins has, and it has worse ability to filter powerline frequencies than the ability Pan-Tompkins has. Therefore, this study started from examining OSEA, then improve its filters and signal-noise divide, finally tried to integrate other R-peak detection methods.
    Due to its general availability and clinical importance, the MIT-BIH Arrhythmia Database（MITDB） is used in this study as the test material. In the presence of power line frequency, EMG, motion artifact, large P wave and sharp T wave, this study tries to use different filtering passbands and other parameters to lower the detection error rate（DER）. First, the CIC（cascaded integrator and comb）filter was used as a building block for both lowpass and highpass filters. Then curve fitting was used to obtain slope information and energy calculation was used to rectify the differentiated signal. Blanking, amplitude thresholding, and slope were used to discriminate between signal and noise.
    After adjusting the parameters, OSEA reveals lower DER, but its parameters are needed to be adjusted according to the feature of the signal in practice(e.g. the updating coefficient of threshold and the length of moving average). During integrating algorithms, it is found that Rayleigh’s energy can suppress noise, but it yields more FNs（false negatives）; on the contrary, Shannon energy yields lower FNs, but it raises more FPs（false positives）as well as large differences in DER. As compared with OSEA, the filters in this study have higher noise suppression ability (e.g. motion artifact) and DER can be even lower if the proposed detection structure is used.

    目錄 中文摘要 I ABSTRACT II 誌謝 III 目錄 IV 圖目錄 VI 表目錄 IX 第1章、 緒論 1 1.1 動機與目的 1 1.2 文獻探討 2 1.3 論文架構 16 第2章、 背景與原理 17 2.1 心電圖 17 2.2 演算法 22 2.2.1 Pan-Tompkins與Hamilton 22 2.2.2 Rufas-Carrabina 26 2.2.3 Kathirvel et al 29 第3章、 研究方法 31 3.1 系統設計 31 3.1.1 軟體架構 31 3.1.2 前處理架構 31 3.1.3 後處理架構 32 3.2 前處理 37 3.2.1 低通濾波器 37 3.2.2 高通濾波器 41 3.2.3 微分器 46 3.2.4 非線性轉換 58 3.2.5 前處理統整 63 3.2.5.1 低通濾波器 63 3.2.5.2 高通濾波器 64 3.2.5.3 微分器 64 3.2.5.4 移動平均（非線性轉換後濾除雜訊） 65 3.2.5.5 濾波結果與頻率響應 65 3.3 後處理 69 3.3.1 區域極大值 70 3.3.2 閥值 72 3.3.3 基準線飄移 75 3.3.4 T波判別 78 3.3.5 Searchback 79 3.3.6 後處理統整 82 第4章、 實驗方法與結果 84 4.1 實驗方法 84 4.1.1 MITDB簡介 84 4.1.2 驗證步驟 85 4.2 實驗結果 85 4.2.1 參數定義 85 4.2.2 濾波器類型的比較 87 4.2.3 演算法對MITDB之偵測結果 88 第5章、 問題與討論 94 第6章、 結論與未來展望 97 6.1 結論 97 6.2 未來展望 98 參考文獻 99   圖目錄 圖 1 1、常用的前處理方法[22][23] 4 圖 1 2、二連律（bigeminy）[57] 5 圖 1 3、三連律（trigeminy）[57] 6 圖 1 4、產生波封訊號的四種方式[27] 8 圖 1 5、劇烈基準線飄移之前處理過程 （a為原始訊號，b為a經過negative fractional-order integration，c為b經過positive fractional-order differentiator，d為b與c經過運算後的特徵訊號）[30] 9 圖 1 6、零跨越點的偵測原理[25] 10 圖 1 7、MITDB基準線飄移之範例[30] 11 圖 1 8、ECG, QRS complex與雜訊的相對功率頻譜[63][65] 12 圖 1 9、四種類型的QRS complex的頻帶 （V：ventricular extra-systole, R and L：right and left bundle branch blocks）[40] 12 圖 1 10、MITDB的頻率成分分布[14] 13 圖 1 11、不同波形的小波係數[39][66] 14 圖 2 1、ECG各導程之波形[57] 17 圖 2 2、ECG波形與動作電位的產生和傳導的關聯[69] 18 圖 2 3、極性為負的QRS complex[14] 19 圖 2 4、QRS complex的可能形狀[55] 20 圖 2 5、EMG範例[14] 21 圖 2 6、PT[8]與Hamilton[11]的前處理架構 22 圖 2 7、PT前處理各階段的波形[8] 23 圖 2 8、Hamilton[11]前處理各階段的波形 24 圖 2 9、PT[8]的後處理架構 25 圖 2 10、Hamilton[11]的後處理架構 25 圖 2 11、Hamilton[11]的訊號分類流程 26 圖 2 12、RC的演算法架構[20] 27 圖 2 13、RC各階段的波形[20] 28 圖 2 14、Kathirvel et al.的演算法架構[21] 29 圖 2 15、Kathirvel et al.各階段的波形[21] 30 圖 3 1、軟體架構 31 圖 3 2、本研究的前處理架構 32 圖 3 3、本研究的後處理架構 32 圖 3 4、本研究的訊號分類流程 34 圖 3 5、以0.5*amp_th為分水嶺對於114m之偵測範例 35 圖 3 6、以0.5*amp_th為分水嶺對於201m之偵測範例 35 圖 3 7、以amp_th為分水嶺對於114m之偵測範例 36 圖 3 8、以amp_th為分水嶺對於201m之偵測範例 36 圖 3 9、低通濾波器（使用t=10ms） 39 圖 3 10、低通濾波器（使用t=20ms） 40 圖 3 11、低通濾波器（使用t=30ms） 40 圖 3 12、高通濾波器（使用t=10ms） 42 圖 3 13、Filter subtraction[58] 42 圖 3 14、高通濾波器（透過filter subtraction並使用t=80ms） 43 圖 3 15、高通濾波器（透過filter subtraction並使用t=70ms） 44 圖 3 16、高通濾波器（透過filter subtraction並使用t=60ms） 44 圖 3 17、帶通濾波器 （低通使用t=20ms，高通透過filter subtraction並使用t=70ms） 45 圖 3 18、帶通濾波器 （低通使用t=20ms，高通透過filter subtraction並使用t=60ms） 46 圖 3 19、數值微分的頻率響應 50 圖 3 20、Savitzky-Golay differentiator（Fs=360Hz, L=2, and t=5ms） 54 圖 3 21、PT[8]的微分器頻率響應（L=2, t=5ms） 55 圖 3 22、Hamilton[11]的微分器頻率響應（L=1, t=5ms） 55 圖 3 23、t調大後的頻率響應（L=2, t=10ms） 56 圖 3 24、係數改變後的頻率響應（L=3, t=10ms） 57 圖 3 25、係數改變後的頻率響應（L=2, t=10ms） 57 圖 3 26、帶通濾波器與微分器（L=2, t=10ms）串接 58 圖 3 28、Fluke ProSim 3 Vital Signs Simulators[76] 59 圖 3 29、特徵點間距和心律的關係 61 圖 3 30、使用Rayleigh's energy面對101m振幅較小的波封之偵測失敗範例 61 圖 3 31、使用Rayleigh's energy面對113m的波封之排除範例 62 圖 3 32、使用Shannon energy面對101m的波封之偵測成功範例 62 圖 3 33、使用Shannon energy面對113m的波封之失敗範例 63 圖 3 34、前處理各階段的波形 66 圖 3 35、PT[8]的濾波頻帶 67 圖 3 36、Hamilton[11]的濾波頻帶 68 圖 3 37、本研究的濾波頻帶 69 圖 3 38、搜尋區域極大值的方法 71 圖 3 39、時間遮沒對108m之large P-wave的效果 72 圖 3 40、前處理與閥值對於113m尖銳的T波之偵測範例 73 圖 3 41、使用疊代的更新方式來面對突波 74 圖 3 42、使用中值的更新方式來面對突波 74 圖 3 43、使用本研究的更新方式面對突波 75 圖 3 45、判定基準線飄移與searchback機制的成功範例（原始訊號v.s.波封訊號） 77 跟「3.3.2閥值」不同的是，這裡只記錄大於振幅閥值的區域極大值的斜率及準位，準位的更新方式則相同，但閥值之更新係數有二，對於大於振幅閥值之區域極大值為0.75，對於大於振幅閥值的一半而小於振幅閥值之區域極大值為0.5（判定是否為searchback候選者）。 78 圖 3 47、T波判別判定雜訊的成功範例 78 圖 3 48、本研究之searchback對106m之三連律偵測成功範例 80 圖 3 49、本研究之searchback對201m之wider QRS complex偵測成功範例 80 圖 3 50、本研究之searchback對217m之二連律偵測成功範例 81 圖 3 51、Hamilton之searchback對106m之三連律偵測失敗範例 81 圖 3 52、Hamilton之searchback對201m之二連律偵測失敗範例 82 圖 4 1、心臟的撲動和顫動[57] 86 圖 5 1、Kathirvel et al.[21]對於200bpm的偵測結果 95 圖 5 2、Kathirvel et al.[21]對於220bpm的偵測結果 96 圖 6 1、曲線化簡法對基準線飄移的作用 98   表目錄 表 1 1、對MITDB驗證的演算法之錯誤率比較 （其文獻有提到即時演算法且錯誤率低於0.4％的的演算法加網底表示） 15 表 3 1、模擬器的ECG特徵點間距（單位:sample，取樣頻率200Hz，加網底表示QRS與移動平均之窗格長度相關、PR和RT與時間遮沒相關） 60 表 3 2、低通濾波器之參數表 64 表 3 3、高通濾波器之參數表 64 表 3 4、微分器之參數表 65 表 3 5、移動平均之參數表 65 表 3 6、濾波頻帶之統整表（粗體字表示接近Elgendi[14]的建議範圍：8-20Hz） 69 表 3 7、後處理統整表 83 表 4 1、濾波器類型v.s.錯誤率 88 表 4 2、Hamilton[11]的驗證結果（錯誤率大於0.5％的記錄以橘色網底表示） 89 表 4 3、本研究的驗證結果（錯誤率大於0.5％的記錄以橘色網底表示） 90 表 4 4、RC[20]的驗證結果（錯誤率大於0.5％的記錄以橘色網底表示） 91 表 4 5、Kathirvel et al.[21]的驗證結果（錯誤率大於0.5％的記錄以橘色網底表示） 92 表 4 6、各演算法對於訊號品質差的記錄之統整（V表示錯誤率小於0.5％，未標示則否） 93 表 5 1、演算法驗證比較（粗體字為較佳的結果） 94

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