고차통계와 Hermite 모델을 이용한 계층적 심전도 비트 분류

박관수; 이종실; 지영준; 김인영; 김선일; 조백환; 이도훈; 송수화

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자료유형: 학술저널

저자정보: 박관수 이종실 지영준 김인영 김선일 조백환 이도훈 송수화

저널정보: 대한의료정보학회 Healthcare Informatics Research Healthcare Informatics Research 제15권 제1호

발행연도: 2009.1

수록면: 117 - 131 (15page)

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Objective: 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. Methods: 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. Results: 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. Conclusion: This research suggests that the Hermite model of the higher order statistics is feasible for heartbeat feature extraction. The hierarchical classification is also feasible for heartbeat classification tasks that have the unbalanced data distribution.

#Electrocardiogram #Higher Order Statistics #Hermite Basis Function #Support Vector Machine #Hierarchical classification

참고문헌 (16)

참고문헌 신청

/ 1998 / American National Standard. Testing and reporting performance results of cardiac. ANSI/AAMI EC57 : 1998/(R)2003

Casasent D / 2005 / A hierarchical classifier using new support vector machines for automatic target recognition / Neural networks 18 : 541 ~ 548

Chazal P / 2004 / Automatic Classification of Heartbeats Using ECG Morphology and Heartbeat Interval Features / IEEE Trans Biomed Eng 51 : 1196 ~ 1206

Elkan C / 2001 / The foundation of cost-sensitive learning / Proceeding of the 17th International Joint Conference on Artificial Intelligence

Golub G / 1991 / Matrix Computations / Johns Hopkins University

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