Heartbeat Classification Using Morphological and Dynamic Features of ECG Signals

• Tipo: Artigo em Revista Científica Internacional
• Data: 2012

Autores

Can Ye (Autor) Outra; Vijaya V K V Kumar (Autor) Outra; Miguel Tavares Coimbra (Autor) FCUP

Revista

Título: IEEE Transactions on Biomedical Engineering

Vol. 59

Páginas: 2930-2941

ISSN: 0018-9294

Editora: IEEE

Indexação

ISI Web of Knowledge - 327 Citações

Scopus - 406 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Biotecnologia ambiental

Outras Informações

ID Authenticus: P-002-54A

DOI: 10.1109/tbme.2012.2213253

Abstract (EN): In this paper, we propose a new approach for heartbeat classification based on a combination of morphological and dynamic features. Wavelet transform and independent component analysis (ICA) are applied separately to each heartbeat to extract morphological features. In addition, RR interval information is computed to provide dynamic features. These two different types of features are concatenated and a support vector machine classifier is utilized for the classification of heartbeats into one of 16 classes. The procedure is independently applied to the data from two ECG leads and the two decisions are fused for the final classification decision. The proposed method is validated on the baseline MITBIH arrhythmia database and it yields an overall accuracy (i.e., the percentage of heartbeats correctly classified) of 99.3% (99.7% with 2.4% rejection) in the "class-oriented" evaluation and an accuracy of 86.4% in the "subject-oriented" evaluation, comparable to the state-of-the-art results for automatic heartbeat classification.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12