Intelligent Arrhythmia Detection and Classification Using ICA

Author

Azemi, Asad ; Sabzevari, Vahid R. ; Khademi, Morteza ; Gholizade, Hossein ; Kiani, Arman ; Dastgheib, Zeinab S.

Author_Institution

Eng. Dept., Pennsylvania State Univ., University Park, PA

fYear

2006

fDate

Aug. 30 2006-Sept. 3 2006

Firstpage

2163

Lastpage

2166

Abstract

In this paper a novel approach for cardiac arrhythmias detection is proposed. The proposed method is based on using independent component analysis (ICA) and wavelet transform to extract important features. Using the extracted features different machine learning classification schemas, MLP and RBF neural networks and K-nearest neighbor, are used to classify 274 instance signals from the MIT-BIH database. Simulations show that multilayer neural networks with Levenberg-Marquardt (LM) back propagation algorithm provide the optimal learning system. We were able to obtain 98.5% accuracy, which is an improvement in comparison with the similar works

Keywords

backpropagation; electrocardiography; feature extraction; independent component analysis; medical signal detection; medical signal processing; multilayer perceptrons; muscle; radial basis function networks; signal classification; wavelet transforms; ECG; ICA; K-nearest neighbor classification scheme; Levenberg-Marquardt back propagation algorithm; MIT-BIH database; MLP; RBF neural networks; cardiac arrhythmia classification; feature extraction; independent component analysis; intelligent arrhythmia detection; machine learning classification scheme; multilayer neural networks; optimal learning system; wavelet transform; Feature extraction; Independent component analysis; Learning systems; Machine learning; Machine learning algorithms; Multi-layer neural network; Neural networks; Spatial databases; Wavelet analysis; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE

Conference_Location

New York, NY

ISSN

1557-170X

Print_ISBN

1-4244-0032-5

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/IEMBS.2006.259292

Filename

4462217