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Healthc Inform Res > Volume 15(1); 2009 > Article
Journal of Korean Society of Medical Informatics 2009;15(1):117-131.
DOI: https://doi.org/10.4258/jksmi.2009.15.1.117    Published online March 31, 2009.
Hierarchical Classification of ECG Beat Using Higher Order Statistics and Hermite Model
Kwan Soo Park1, Baek Hwan Cho3, Do Hoon Lee1, Su Hwa Song1, Jong Shill Lee1, Young Joon Chee2, In Young Kim1, Sun I Kim1
1Department of Biomedical Engineering, Hanyang University, Korea.
2Department of Biomedical Engineering, College of Engineering, University of Ulsan, Korea.
3Department of Biomedical Engineering, Carnegie Mellon University, USA.
Correspondence:  In Young Kim,
Received: 10 November 2008

Objective: The heartbeat classification of the electrocardiogram is important in cardiac disease diagnosis. For detecting QRS complex, conventional detection algorithmhave been designed to detect P, QRS, Twave, 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 conventionalmulticlass classificationmethodmay have skewed results to themajority 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 andHermitemodel 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 conventionalmulticlass classificationmethod (46.16%). In addition, theHermitemodel 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.

Key Words: Electrocardiogram, Higher Order Statistics, Hermite Basis Function, Support VectorMachine, Hierarchical classification
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