고차통계와 Hermite 모델을 이용한 심전도 비트의 계층적 분류

Abstract

The heartbeat classification of the electrocardiogram is important in cardiac disease diagnosis. For detecting QRS complex, conventional detection algorithm have been designed to detect P, QRS, T wave, first. However, the detection of the P and T wave is difficult because their amplitudes are relatively low, and occasionally they are included in noise. Furthermore the conventional multiclass classification method may have skewed results to the majority class, because of unbalanced data distribution. The Hermite model of the higher order statistics is good characterization methods for recognizing morphological QRS complex. We applied three morphological feature extraction methods for detecting QRS complex: higher order statistics, Hermite basis functions and Hermite model of the higher order statistics. Hierarchical scheme tackle the unbalanced data distribution problem. We also employed a hierarchical classification method using support vector machines. We compared classification methods with feature extraction methods. As a result, our mean values of sensitivity for hierarchical classification method (75.47%, 76.16% and 81.21%) give better performance than the conventional multiclass classification method (46.16%). In addition, the Hermite model of the higher order statistics gave the best results compared to the higher order statistics and the Hermite basis functions in the hierarchical classification method. This research suggests that the Hermite model of the higher order statistics is feasible for heart beat feature extraction. The hierarchical classification is also feasible for heart beat classification tasks that have the unbalanced data distribution.