Hierarchical Classification of ECG Beat Using Higher Order Statistics and Hermite Model / 대한의료정보학회지
Journal of Korean Society of Medical Informatics
;
: 117-131, 2009.
Artigo
em Coreano
| WPRIM
| ID: wpr-83078
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.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Classificação
/
Diagnóstico
/
Eletrocardiografia
/
Máquina de Vetores de Suporte
/
Cardiopatias
/
Ruído
Tipo de estudo:
Estudo diagnóstico
Idioma:
Coreano
Revista:
Journal of Korean Society of Medical Informatics
Ano de publicação:
2009
Tipo de documento:
Artigo
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