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:676 下載:1 |
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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.
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