Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest

Premature ventricular contraction (PVC) is one of the most common arrhythmias in the clinic. Due to its variability and susceptibility, patients may be at risk at any time. The rapid and accurate classification of PVC is of great significance for the treatment of diseases. Aiming at this problem, th...

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Detalles Bibliográficos
Autores principales: Xie, Tiantian, Li, Runchuan, Shen, Shengya, Zhang, Xingjin, Zhou, Bing, Wang, Zongmin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2019
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6800940/
https://www.ncbi.nlm.nih.gov/pubmed/31687121
http://dx.doi.org/10.1155/2019/5787582
Descripción
Sumario:Premature ventricular contraction (PVC) is one of the most common arrhythmias in the clinic. Due to its variability and susceptibility, patients may be at risk at any time. The rapid and accurate classification of PVC is of great significance for the treatment of diseases. Aiming at this problem, this paper proposes a method based on the combination of features and random forest to identify PVC. The RR intervals (pre_RR and post_RR), R amplitude, and QRS area are chosen as the features because they are able to identify PVC better. The experiment was validated on the MIT-BIH arrhythmia database and achieved good results. Compared with other methods, the accuracy of this method has been significantly improved.

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