International Journal of Electrical, Electronics and Data Communication (IJEEDC)
eISSN:2320-2084 , pISSN:2321-2950
Follow Us On :
current issue
Volume-11,Issue-8  ( Aug, 2023 )
  1. Volume-11,Issue-7  ( Jul, 2023 )
  2. Volume-11,Issue-6  ( Jun, 2023 )
  3. Volume-11,Issue-5  ( May, 2023 )

Statistics report
Dec. 2023
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 128
Paper Published : 1688
No. of Authors : 4671
  Journal Paper

Paper Title :
Performance Analysis Of Feature Extraction Schemes For ECG Signal Classification

Author :Devashree Joshi, Rajesh Ghongade

Article Citation :Devashree Joshi ,Rajesh Ghongade , (2013 ) " Performance Analysis Of Feature Extraction Schemes For ECG Signal Classification " , International Journal of Electrical, Electronics and Data Communication (IJEEDC) , pp. 45-51, Volume-1,Issue-5

Abstract : Electrocardiogram (ECG) is the P, QRS, T wave indicating the electrical activity of the heart. Electrocardiogram is the most easily accessible bioelectric signal that provides the doctors with reasonably accurate data regarding the patient heart condition. Many of the cardiac problems are visible as distortions in the electrocardiogram (ECG). Normally ECG related diagnoses are carried out manually. As 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. In this work we propose computer based automated system to help the doctor to detect cardiac arrhythmia. As reference, we have used the Normal, Premature Ventricular Contraction (PVC) and Fusion signals of the MIT-BIH Database. Then we have focused on the various schemes for extracting the useful features of the ECG signals for use with artificial neural networks. We extract the principal characteristics of the signal by means of the Principal Component Analysis (PCA) technique and other techniques such as Discrete Wavelet Transform and Discrete Cosine Transform. After signal pre-processing, they are applied to an Artificial Neural Network Multilayer Perceptron (ANN MLP). The task of an ANN based system is to correctly identify the three classes the feature extraction schemes are discussed and compared with RBFN & Support Vector Machine in this work.

Type : Research paper

Published : Volume-1,Issue-5


Copyright: © Institute of Research and Journals

| PDF |
Viewed - 39
| Published on 2014-01-20
IRAJ Other Journals
IJEEDC updates
Volume-11,Issue-7(July ,2023) Want to join us ? CLick here
The Conference World



Technical Editor, IJEEDC
Department of Journal and Publication
Plot no. 30, Dharma Vihar,
Khandagiri, Bhubaneswar, Odisha, India, 751030
Mob/Whatsapp: +91-9040435740