以適應性特徵擷取及改進支持向量機檢測心電圖心律不整

Detection of cardiac arrhythmia in electrocardiograms using adaptive feature extraction and modified support vector machines

楊岳穎(Yueh-Yiing Yang)

指導教授 ： 高文忠

國立臺灣師範大學/科技學院/應用電子科技學系/碩士(2010年)

摘要

心電圖（ECG）分析是檢測心律不整最好的方法之ㄧ，雖然已經有許多相關的演算法已經被提出，但是可靠性低的訊號特徵提取分析或歸納能力較低的辨識器使得系統的辨識率仍然不能達到要求。本論文提出適應性特徵擷取與改良的支持向量機(SVMs)的心電圖心律不整檢測系統。首先利用小波轉換係數及訊號之振幅或週期等參數作為系統的候選人，針對每一個分類器適應性的擷取出少數特定的特徵；而改良式支持向量機結合k-means分群法與一對一支持向量機，並且修改其投票機制，進一步提高了相似類別之間的辨識率。此心電圖心律不整檢測系統使用了超過100,000筆的MIT-BIH心律不整資料庫樣本進行測試，平均辨識率高達97.96%。

關鍵字

心電圖 ； 適應性特徵選取 ； 支持向量機 ； k-means分群法

並列摘要

The electrocardiogram (ECG) analysis is one of the most important approaches to cardiac arrhythmia detection. Many algorithms have been proposed, however, the recognition rate is still unsatisfactory due to unreliable feature extraction in signal characteristic analysis or poor generalization capability of the classifier. In this paper, we propose a system for cardiac arrhythmia detection in ECGs with adaptive feature selection and modified support vector machines (SVMs). Wavelet transform-based coefficients and signal amplitude/interval parameters are first enumerated as candidates, but only a few specific ones are adaptively selected for the classification of each class pair. A new classifier, which integrates k-means clustering, one-against-one SVMs, and a modified majority voting mechanism, is proposed to further improve the recognition rate for extremely similar classes. By testing the system with more than 100,000 samples in MIT-BIH arrhythmia database, the average recognition rate is 97.72%.

並列關鍵字

electrocardiogram (ECG) ； adaptive feature extraction ； support vector machines (SVMs) ； k-means clustering

參考文獻

[1] 行政院衛生署 國民健康局“98年度死因統計完整統計表”。

[2] H. F. Schels, R. Habert, G. Jilge, P. Steinbigler, and G. Steinbeck, “Frequency analysis of the electrocardiogram with maximum entropy method for identification of patients with sustained ventricular tachycardia,” IEEE Trans. Biomed. Eng., vol. 38, no. 9, pp. 821-826, Sep. 1991.

[3] H. A .M.and A. L. Nashash, “A dynamic fourier series for the compression of ECG using FFT and adaptive coefficient estimation,” Medical Engineering & Physics, vol. 17, no. 3, pp. 197-203, April 1995.

[4] J. S. Paul , M. R. S. Reddy, and V. Jagadeesh Kumar, “A cepstral transformation technique for dissociation of Wide QRS type arrhythmia signals using DCT,” Signal Processing, vol. 76, pp. 123-131, 1999.

[5] I. S. N. Murthy and G. S. S. Durga Prasad, “Analysis of ECG from pole-zero models,” IEEE Trans. Biomed. Eng., vol. 39. no. 7, pp. 741-751, July 1992.