NDLI: Performance Analysis of Feature Extraction Schemes for Artificial Neural Network Based ECG Classification

Content Provider	IEEE Xplore Digital Library
Author	Ghongade, R. Ghatol, A.A.
Copyright Year	2007
Description	Author affiliation: Vishwakarma Inst. of Inf. Technol., Pune (Ghongade, R.)
Abstract	Many of the cardiac problems are visible as distortions in the electrocardiogram (ECG). Since the abnormal heart beats can occur randomly it becomes very tedious and time-consuming to analyze say a 24 hour ECG signal, as it may contain hundreds of thousands of heart beats. Hence it is desired to automate the entire process of heart beat classification and preferably diagnose it accurately In this paper the authors have focused on the various schemes for extracting the useful features of the ECG signals for use with artificial neural networks. Arrhythmia is one such type of abnormality detectable by an ECG signal. The three classes of ECG signals are Normal, Fusion and Premature Ventricular Contraction (PVC). The task of an ANN based system is to correctly identify the three classes, most importantly the PVC type, this being a fatal cardiac condition. Discrete Fourier Transform, Principal Component Analysis, and Discrete Wavelet Transform and Discrete Cosine Transform are the four schemes discussed and compared in this paper. For comparison the statistical techniques like linear discriminant analysis and tree clustering are also evaluated.
Starting Page	486
Ending Page	490
File Size	335388
Page Count	5
File Format	PDF
ISBN	0769530508
DOI	10.1109/ICCIMA.2007.221
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-13
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Performance analysis Feature extraction Artificial neural networks Electrocardiography Heart beat Discrete Fourier transforms Discrete wavelet transforms Signal analysis Signal processing Heart rate variability
Content Type	Text
Resource Type	Article

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