계층구조적 분류모델을 이용한 심전도에서의 비정상 비트 검출Detection of Abnormal Heartbeat using Hierarchical Classification in ECG
- Other Titles
- Detection of Abnormal Heartbeat using Hierarchical Classification in ECG
- Authors
- 이도훈; 조백환; 박관수; 송수화; 이종실; 지영준; 김인영; 김선일
- Issue Date
- Dec-2008
- Publisher
- 대한의용생체공학회
- Keywords
- arrhythmia detection; unbalanced data distribution; hierarchical classification; domain knowledge; support vector machine
- Citation
- 의공학회지, v.29, no.6, pp.466 - 476
- Indexed
- KCI
- Journal Title
- 의공학회지
- Volume
- 29
- Number
- 6
- Start Page
- 466
- End Page
- 476
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/177535
- ISSN
- 1229-0807
- Abstract
- The more people use ambulatory electrocardiogram (ECG) for arrhythmia detection, the more researchers report the automatic classification algorithms. Most of the previous studies donʼt 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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