PVC Arrhythmia Detection Using Neural Networks

Author

Gharaviri, Ali ; Teshnehlab, Mohammad ; Moghaddam, Hamid Abrishami

Author_Institution

K.N.Toosi Univ. of Technol., Tehran

fYear

2007

fDate

27-29 Sept. 2007

Firstpage

234

Lastpage

237

Abstract

Premature ventricular contraction (PVC) beats are of great importance in evaluating and predicting life threatening ventricular arrhythmias. The aim of this study is to improve the diagnosis level of detection of PVC arrhythmia from ECG signals. This improvement is based on an appropriate choice of features for the selected task. We extracted fourteen features including, temporal, morphological features from MIT/BIH ECG signals database and then applying LDA algorithm, we reduced them into nine features. Using a neural network for classification, satisfactory results obtained with an accuracy rate of 99.8%.

Keywords

electrocardiography; feature extraction; medical signal processing; neural nets; noise; signal classification; ECG signals; LDA algorithm; PVC heartbeat; cardiac arrhythmias; extraction scheme; feature extraction; feature selection; neural networks; noise removal filtering; premature ventricular contraction arrhythmia detection; signal classification; Electrocardiography; Feature extraction; Filtering; Heart beat; Heart rate variability; Hospitals; Linear discriminant analysis; Neural networks; Patient monitoring; Spatial databases;

fLanguage

English

Publisher

ieee

Conference_Titel

Image and Signal Processing and Analysis, 2007. ISPA 2007. 5th International Symposium on

Conference_Location

Istanbul

ISSN

1845-5921

Print_ISBN

978-953-184-116-0

Type

conf

DOI

10.1109/ISPA.2007.4383696

Filename

4383696