改良式經驗模組分解函數應用於心電圖訊號雜訊濾除之探討

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Title:	改良式經驗模組分解函數應用於心電圖訊號雜訊濾除之探討
Authors:	張剛鳴
Contributors:	資訊學院;光電與通訊學系?
Keywords:	總體經驗模組分解;心電圖雜訊濾除;本質模組函數;ECG;EMD;EEMD;noise filtering
Date:	2011
Issue Date:	2013-07-18 07:35:01 (UTC+0)
Abstract:	經驗模組分解（Empirical Mode Decomposition， EMD）是基於希爾伯特-黃轉換（Hilbert-Huang Transformation，HHT）內的演算法。經由EMD 分解會將訊號依序分解成從高頻率到低頻率排序的函數，稱為本質模組函數（Intrinsic Mode Function，IMF）。由於較低階的IMF 包含高頻率訊號成分，反之亦然。刪除較低階的IMF 訊號可以刪除高頻雜訊，刪除較高階的IMF 訊號可以刪除低頻雜訊。因此有效選擇擬刪除的IMF 函數階層，就可以達到濾波器的效果。EMD 用於濾波時有一個主要缺點，那就是模組函數混合效應(mode mixing effect)。簡言之，相間隔的IMF 會同時存在類似成份的訊號。因此，總體經驗模組分解（Ensemble Empirical Mode Decomposition，EEMD）的引入可減少模組函數混合效應。EEMD 的原理就是添加白雜訊訊號到試驗訊號中，並進行後續EMD 分解，保留每次求出的IMF 函數。重覆多次此步驟後，將不同次添加白雜訊訊號所分解的 IMF，以相同階數IMF 相加。因此，隨著越來越多的試驗次數增加，可以將訊號成份更有效保留，進而減少模組函數混合效應。本研究的目的是探討以EEMD 進行心電圖雜訊濾除的表現。同時比較Butterworth 濾波器、Wiener 濾波器與Savitzky-Golay 濾波器。雜訊部分肌肉收縮的高頻雜訊，50 Hz 的電源線干擾雜訊和基準線漂移的低頻雜訊。並分別以訊號形狀失真度為濾波效果指標比較。初步結果顯示EEMD 優於其他濾波器功能。 Empirical mode decomposition (EMD) is a novel algorithm developed recently. EMD is based on the decomposition derived from the data and is useful for the analysis on nonlinear and nonstationary time series signals. With iterative decomposition of signals, EMD separated the full signal into ordered elements that with frequency ranged from higher to lower frequencies on each intrinsic mode function (IMF) levels. The major disadvantage of EMD is mode mixing effect. Mode mixing indicates that oscillations of different time scales coexist in a given IMF, or that oscillations with the same time scale have been assigned to different IMFs. Hence, ensemble EMD (EEMD) was introduced to remove the mode-mixing effect. The principle of the EEMD is to add white noise into the signal with many trials. Noise in each trial is different, and the added noise can be canceled out on an average, if the trial number is sufficient. Thus, the residual part is the signal, as more and more trials are added in the ensemble. In this study, a novel noise filtering algorithm is proposed based on ensemble empirical mode decomposition (EEMD) to remove artifacts within ECG. Three noise patterns, 50 Hz, EMG, and base line wander with different power were embedded into the simulated ECG signal. Traditional IIR filter, Wiener filter, empirical mode decomposition (EMD) and EEMD were used to compare filtering performance. Phase delay and the mean square error between clean simulated ECG and filtered ECG were used as filtering performance index. Results showed both nearly zero phase delay and high noise filtering are major advantage of EEMD based filter, especially on arrhythmia ECG.
Appears in Collections:	[Department of Photonics and Communication Engineering] Ministry of Science and Technology Research Project

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