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KMID : 1137820080290060466
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의공학회지
2008 Volume.29 No. 6 p.466 ~ p.476
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Detection of Abnormal Heartbeat using Hierarchical Qassification in ECG
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Lee Do-Hoon
Cho Baek-Hwan
Park Kwan-Soo
Song Su-Hwa
Lee Jong-Shill
Chee Young-Joon
Kim In-Young
Kim Sun-Il
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Abstract
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The more people use ambulatory electrocardiogram(ECG) for arrhythmia detection, the more researchers report the automatic classification algorithms. Most of the previous studies don’t consider the un-balanced data distribution. Even in patients, there are much more normal beats than abnormal beats among the data from 24 hours. To solve this problem, the hierarchical classification using 21 features was adopted for arrhythmia abnormal beat detection. The features include R-R intervals and data to describe the morphology of the wave. To validate the algorithm, 44 non-pacemaker recordings from physionet were used. The hierarchical classification model with 2 stages on domain knowledge was constructed. Using our suggested method, we could improve the performance in abnormal beat classification from the conventional multi-class classification method. In conclusion, the domain knowledge based hierarchical classification is useful to the ECG beat classification with unbalanced data distribution.
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KEYWORD
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arrhythmia detection, unbalanced data distribution, hierarchical classification, domain knowledge, support vector machine
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