ANALYSIS OF ELECTROCARDIOGRAM SIGNALS OF ARRHYTHMIA AND ISCHEMIA USING FRACTAL AND STATISTICAL FEATURES

作者：Don S; Chung Duckwon; Min Dugki^*; Choi Eunmi

来源：Journal of Mechanics in Medicine and Biology, 2013, 13(1): 1350008.

DOI：10.1142/S0219519413500085

• Electrocardiogram
• fractal dimension
• spectral entropy
• k-NN
• GMM classifier

In this study, we present a three-stage method for detecting abnormalities and classifying electrocardiogram (ECG) beats using a k-nearest neighbor (k-NN) classifier and Gaussian mixture model (GMM). In the first stage, a signal filtering method is used to remove the ECG beat baseline wander. In the second stage, features are extracted based on Higuchi%26apos;s fractal dimension (HFD) and statistical features. In the third stage, k-NN and GMM are used as classifiers to classify arrhythmia and ischemia. A total of 30,000 ECG segments obtained from the MIT-BIH Arrhythmia and European ST-T Ischemia databases were used to quantify this approach. 60% of the beats were used for training the classifier and the remaining 40%, for validating it. An overall accuracy of 99% and 98.24% was obtained for k-NN and GMM, respectively. This result is significantly better than that of currently used state-of-the-art classification approaches for arrhythmia and ischemia.

• 出版日期2013-2