Shijie Zhou
Arrhythmias Classification, Histogram Feature Extraction,Average Absolute Deviation, Coarse-graining ProcessingAnalysis
In this paper, a novel, and computationally fast method was proposed to classify ventricular tachycardia (VT) and ven- tricular fibrillation (VF) by using histogram and average absolute deviation. To begin with, a time-domain method based histogram, was addressed to refine the general fea- tures that illustrate the density of amplitude distribution of a window length long ECG signal in successive numerical intervals of equal size. Besides, segments of monomorphic VT randomly collected from the MIT-BIH Malignant Ar- rhythmia subset are calculated by the histogram function for obtaining the reference histogram with different length. Histogram differences in case of monomorphic VT or VF calculated as features to generate a threshold for arrhyth- mias distinction. Then, an average absolute deviation al- gorithm is applied to obtain a deviation value that is com- pared to the threshold for monomorphic VT or VF classifi- cation. The novelty of this method is that ECG signal statis- tics, morphological analysis, the histogram of signal (den- sity estimation) and average absolute deviation altogether have been used to achieve a higher classification rate. The test shows that the detection accuracy for monomorphic VT, and VF is approximate 100% for a test set of MIT- BIH database (98 monomorphic VT, and 128 VF). Com- pared with LZ (Lempel-Ziv) complexity measure based on time-domain analysis, the proposed method has high per- formance, low computational complexity, and will be able to well implement on ECG Tele-monitoring analysis sys- tems.
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