고차통계와 Hermite 모델을 이용한 계층적 심전도 비트 분류
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 박관수 | - |
dc.contributor.author | 조백환 | - |
dc.contributor.author | 이도훈 | - |
dc.contributor.author | 송수화 | - |
dc.contributor.author | 이종실 | - |
dc.contributor.author | 지영준 | - |
dc.contributor.author | 김인영 | - |
dc.contributor.author | 김선일 | - |
dc.date.accessioned | 2022-12-20T22:53:49Z | - |
dc.date.available | 2022-12-20T22:53:49Z | - |
dc.date.created | 2022-09-19 | - |
dc.date.issued | 2009-03 | - |
dc.identifier.issn | 2093-3681 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/177073 | - |
dc.description.abstract | 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. | - |
dc.language | 한국어 | - |
dc.language.iso | ko | - |
dc.publisher | 대한의료정보학회 | - |
dc.title | 고차통계와 Hermite 모델을 이용한 계층적 심전도 비트 분류 | - |
dc.title.alternative | Hierarchical Classification of ECG Beat Using Higher Order Statistics and Hermite Model | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 김인영 | - |
dc.identifier.doi | 10.4258/jksmi.2009.15.1.117 | - |
dc.identifier.bibliographicCitation | Healthcare Informatics Research, v.15, no.1, pp.117 - 131 | - |
dc.relation.isPartOf | Healthcare Informatics Research | - |
dc.citation.title | Healthcare Informatics Research | - |
dc.citation.volume | 15 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 117 | - |
dc.citation.endPage | 131 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART001328628 | - |
dc.description.journalClass | 2 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Electrocardiogram | - |
dc.subject.keywordAuthor | Higher Order Statistics | - |
dc.subject.keywordAuthor | Hermite Basis Function | - |
dc.subject.keywordAuthor | Support Vector Machine | - |
dc.subject.keywordAuthor | Hierarchical classification | - |
dc.identifier.url | https://www.e-hir.org/journal/view.php?number=551 | - |
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