以總體經驗模態分解法為基礎的心電圖肌電干擾消除演算法

Abstract

心臟血管疾病長久以來一直位居國人十大死因之第二位。心電圖在心血管疾病的診斷上扮演相當重要的角色，利用心電圖檢測心臟疾病是最為簡便的方式之一，因其具有非侵入式、可即時偵測和容易取得...等優點，因此在臨床上被廣泛使用。傳統心電圖是由醫師以肉眼進行診斷，對於受到雜訊干擾的部分，肉眼可輕易跳過，但隨著遠端無線照護系統的發展，即時監控和心電圖初期診斷便是其中一項重要指標，此時心電圖的初步診斷工作便會由程式取代。由於即時監控需長時間記錄心電圖狀況，傳統醫院所採用之標準十二導程心電圖並不適用，因此多以行動心電圖記錄儀器(如單導或三導程之可攜式心電圖記錄器)做為測量工具，方便病人攜掛於身上。然而，一般由行動心電圖記錄儀器所測量之心電圖波形，容易受到環境、病人的動作等生理因素的干擾而影響記錄結果。常見的雜訊如50/60 Hz市電干擾、肌肉收縮所產生之肌電干擾、呼吸所造成之基線漂移以及運動時電極貼片和皮膚間相對摩擦所產生之移動假影。這些干擾的存在，皆會影響醫師或程式的判讀。因此，心電圖雜訊濾除對於心電圖診斷系統而言是非常重要的前置處理步驟。 本論文提出一套結合可適性移動平均濾波器和總體經驗模態分解法之訊號處理架構。可適性移動平均濾波器用來進行基線標移的回復，做為偵測及消除肌電干擾之前置處理，基線漂移復原後的訊號經總體經驗模態分解法進行頻帶拆解後，結合QRS偵測演算法，排除QRS對於結果的影響，僅取出受肌電干擾影響的成分進行雜訊估計與消除，再利用移動窗形變異數，標記出訊號曾受到肌電干擾影響的部位並做處理後之訊號評分，供使用者參考。並以互相關函數、均方根誤差百分比和心電圖特徵誤差為性能指標，檢驗經過上述訊號處理後心電圖的品質。由實驗結果初步證實使用本論文所提出之方法可有效壓抑心電圖中基線漂移和肌電干擾且不會對心電圖特徵造成破壞。

Cardiovascular disease has been listed as the second rank of the top ten leading causes of death. Electrocardiogram(ECG) has played an important role and has been widely used clinically because it is a non-invasive, real-time, quick and easy-to-implement technique. Cardiovascular disease was diagnosed traditionally by inspection from doctors. For doctors, ECG noise can be easily ignored by visual inspection. Nevertheless, with the advance of science and technology, remote monitoring and diagnosis have become important processes to automatically detecting cardiovascular disease. However, in holter devices, ECG recordings are often corrupted by artifacts in some real practice, such as 50/60Hz power line interference, muscle contraction induced electromyogram(EMG), movement(or breath) induced baseline wandering or motion artifact. These aforementioned noises might result in misleading ECG detection. Thus, pre-processing of ECG noise is a very important task in such ECG analysis systems. In this thesis, an effective approach to eliminate baseline wander and EMG noise from ECG based on modified moving average filter and ensemble empirical mode decomposition (EEMD) was proposed. Modified moving average filter is used to eliminate ECG base line drift. It can be viewed as a pre-processing of the EEMD-based EMG reduction method. If data is interfered by EMG noise, EEMD is first used to decompose ECG data into different frequency components. By combination of proper QRS detection algorithms, only noise part will be extracted without affecting QRS complex or other ECG component. Finally, EMG noise can be estimated and removed from original ECG data. Then, by moving variance detection method, EMG positions can be detected and marked as reference to users. Cross correlation coefficient (Corr-Coef), percentage root-mean-square difference (PRD) and ECG morphology were used to examine the artificial data performance of proposed algorithm. Results showed that proposed de-noising framework successfully eliminate baseline wander and EMG interferences without significantly distorting the ECG waveform.