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Publication Type : Journal Article
Publisher : Journal of Mechanics in Medicine and Biology,
Source : Journal of Mechanics in Medicine and Biology, Volume 13, Number 01, p.1350008 (2012)
Url : https://doi.org/10.1142/S0219519413500085
Campus : Amritapuri
School : Department of Computer Science and Engineering, School of Engineering
Department : Computer Science
Year : 2012
Abstract : 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'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.
Cite this Research Publication : Dr. Don S. and Dugki Min, “Analysis of Electrocardiogram Signals of Arrhythmia and Ischemia Using Fractal and Statistical Features”, Journal of Mechanics in Medicine and Biology, vol. 13, p. 1350008, 2012.