根據行政院衛生署統計，民國九十六年國人十大死因排行榜，心臟血管疾病死亡率居第二順位。由於心臟病所引發的心臟血管疾病是無法預期，所以發展一套緊急的警告系統去即時偵測心臟疾病是很重要的，而利用心電圖(Electrocardiogram, ECG)來檢測心臟疾病為最簡便有效，且心電圖有價格便宜、即時偵測和容易取得…等優點，因此在臨床上被廣泛使用。 然而心電圖資料的量測過程中，會受到許多人為因素所造成的雜訊，這些雜訊包括來自電力線的高頻雜訊干擾、肌肉收縮引起的雜訊、由無線電所造成的高頻雜訊和移動所造成的基線飄移(Baseline Wander)…等，而這些雜訊干擾會妨礙醫師診斷上的評估。 本論文提出一個有效的ECG系統去萃取心電訊號，並使用黃鍔所提出的經驗模態分解法(Empirical Mode Decomposition, EMD)去分離周邊雜訊；以經驗模態分解為基礎的方法能夠移除與心電訊號無關的雜訊和修正基線飄移，求得一個失真率低的心電訊號。此方法利用MIT-BIH心律不整資料庫來做模擬驗證，模擬結果說明以經驗模態分解為基礎的方法，能有效的應用於移除與心電訊號無關的雜訊和修正基線飄移。並將此方法在LabVIEW平台上完成，達到即時分析處理心電訊號。 According to statistic of Department of Health, Executive Yuan, R.O.C. in 2007, cardiovascular disease has been listed as the second rank of the top ten leading causes of death. Since cardiovascular disease induced heart attack may often occur in unexpected situation, development of an emergent alarm system for early detection of heart dysfunction is important. One most efficient and easy way for detection of early heart dysfunction is the use of electrocardiogram (ECG). ECG has the advantages of cheap, real-time detection, and easy to implement which been widely used in clinics. The aim of this study is to establish a noise-free real-time cardiac alarm system. Nevertheless, ECG recordings are often corrupted by artifacts in some real practice. There are many artifacts presented in ECG recordings, including power line coupled high-frequency electricity noise, muscle contraction induced noise, high-frequency noise transmitted by surrounding RF equipments, and movement induced baseline wandering (BW). The interference of these aforementioned noises might hinder a doctor’s diagnosis and result in misleading ECG detection. The thesis proposes a novel ECG system to exact ECG signals and separates them from surrounding noise using Dr. Huang's empirical mode decomposition (EMD) method. The proposed EMD-based method is able to remove ECG unrelated noise and correct BW with minimum signal distortion. The method is validated through experiments on the MIT-BIH databases. The simulations show that the proposed EMD-based method provides very good results for removing ECG unrelated noise and correcting BW. The empirical mode decomposition algorithm was also implemented on LabVIEW platform, and achieved real-time analyses of electrocardiac signals.
