以嵌入式收集系統配合非傳統式的分析方法應用於分析心電圖(ECG)訊號之研究

Non-traditional method applied in ECG analysis using embedded collecting system

古展燁(Chan-Yeh Ku)

指導教授 ： 謝建興

元智大學/工程學院/機械工程學系/碩士(2005年)

摘要

心電圖(ECG)在醫療上除了被用來當作診斷相關心臟疾病外，也常被運用於判斷人體的健康與情緒狀態。但一般用於醫療診斷的心電圖機器皆只有抓取到心跳訊號後即時秀圖在螢幕上，並沒有將這些心跳訊號儲存下來，因此無法對這些心跳訊號做更進一步的分析。有鑑如此，本論文主要的目的是要設計可擷取心跳訊號並儲存的系統，共設計出兩套：一套為利用微電腦與電路設計，完成單晶片ECG收集裝置系統；另一套為利用工業級電腦Industrial Personal Computer (IPC)當主體完成的IPC ECG收集裝置系統。此兩套系統皆可擷取龐大的心跳訊號並儲存起來，以利事後的分析。

在本論文後段亦有系統測試結果與心跳訊號分析。系統測試包括：1使用自製完成的單晶片ECG收集裝置系統去收集一個正常人騎腳踏車(獨自騎腳踏車和載人騎腳踏車)環繞校園(騎腳踏車的期間會經過一些小山丘)的心跳訊號；2.收集十個正常人一般情況的心跳以及運動時後的心跳；3.使用IPC ECG收集裝置系統到亞東醫院心臟內科ICU(Intensive Care Unit)抓取AMI(Acute Myocardial Infarction)病人的心跳訊號；而心跳訊號分析方面則是將收集到的心跳訊號以傳統(Power Spectral)及非傳統(Detrended Fluctuation Analysis (DFA) 、Rank Order-Frequency Statistics)的分析方法分析。此系統測試與心跳訊號分析一方面驗證了自製ECG收集裝置的可行性，另一方面也得知Similarity、LFP/HFP、HFP/TF分析會因為受測者外在因素的改變(例如受測者處於一般情況和受測者處於運動情況)而改變其結果，唯有利用DFA理論分析不會因為受測者外在因素的改變而改變其數值。

關鍵字

心電圖 ； 單晶片

並列摘要

Complex physiologic signals may carry unique dynamic signatures that are related to their underlying mechanisms. Based on non-traditional methods, such as detrended fluctuation analysis (DFA) and rank order statistics (ROS) of symbolic sequences, and traditional method, such as power spectral analysis, we applied these methods to heart rate variability (HRV) in intensive care units (ICU) in order to determine which indexes are more accurate to help doctors diagnose patients in an ICU more rapidly in the future. Thirty three patients with 27 light cases and 6 serious cases of acute myocardial infarction (AMI) patients at hospital in ICU were studied as group A. This group was collected electrocardiograph (ECG) signals lasting around 60 min using an industrial personal computer (IPC). Ten college student volunteers as group B for comparison with group A also provided ECG signals lasting around 60 min using PIC microprocessor technology. It was found that DFA can clearly distinguish pathologic states of AMI patients in ICU in comparison with the healthy group. However, the ROS and power spectral analysis are more sensitive to the status of either AMI patients or volunteers.

並列關鍵字

ECG ； Industrial Personal Computer(IPC) ； ICU(Intensive Care Unit) ； AMI(Acute Myocardial Infarction) ； Detrended fluctuation analysis(DFA) ； Rank Order-Frequency Statistics ； Power Spectral

參考文獻

[3]M.L. Sait, A.W. Wood, and H.A. Sadafi, “A study of heart rate and heart rate variability in human subjects expose to occupational levels of 50Hz circular polarised magnetic fields,” Medical Engineering & Physics, 21:361 – 369, 1999

[4]S. Akselrod, D. Gordon, F.A. Ubel, D.C. Shannon, A.C. Barger and R.J. Cohen, "Power spectral analysis of heart rate fluctuation : a quantitative probe of beat-to-beat cardiovascular control," Science, vol. 213, no. 10, pp. 220-222, 1981.

[6]J.P. Saul, Y. Arai, R.D. Berger, L.S. Lilly, W.S. Colucci and R.J. Cohen, "Assessment of autonomic regulation in chronic congestive heart failure by heart rate spectral analysis," Am J Cardiol, vol. 61, pp. 1292-1299, 1988.

[7]K.E.F. Sands, M.L. Appel, L.S. Lilly, F.J. Schoen, G.H. Mudge and R.J. Cohen, "Assessment of heart rate variability in human cardiac transplant recipients using power spectral analysis," Circulation, vol. 79, pp. 76-82, 1989.

[8]A.M. Bianchi, L. Mainardi, E. Petrucci, M.G. Signorini, M. Mainardi and S. Cerutti, “Time-variant power spectral analysis for the detection of transient episodes in HRV signal”, IEEE Trans. Biomedical Engineering, Vol.40, No.2, pp.136-144, 1993.