IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Disease Classification Using ECG Signals Based on R-Peak Analysis With ABC and ANN

Disease Classification Using ECG Signals Based on R-Peak Analysis With ABC and ANN
View Sample PDF
Author(s): Suman Lata (National Institute of Technical Teachers Training and Research, Chandigarh, India)and Rakesh Kumar (National Institute of Technical Teachers Training and Research, Chandigarh, India)
Copyright: 2022
Pages: 22
Source title: Research Anthology on Artificial Neural Network Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-2408-7.ch043

Purchase

View Disease Classification Using ECG Signals Based on R-Peak Analysis With ABC and ANN on the publisher's website for pricing and purchasing information.

Abstract

ECG feature extraction has an important role in identifying a number of cardiac diseases. Lots of work has been done in this field but the most important challenges faced in previous work are the selection of proper R-peaks and R-R intervals due to the lack of appropriate pre-processing steps like decomposition, smoothing, filtering, etc., and the optimization of the features for proper classification. In this article, DWT-based pre-processing and ABC is used for optimization of features which helps to achieve better classification accuracy. It is utilized for initial diagnosis of abnormalities. The signals are taken from MIT-BIH arrhythmia database for the analysis. The aim of the research is to classification of six diseases; Normal, Atrial, Paced, PVC, LBBB, RBBB with an ABC optimization algorithm and an ANN classification algorithm on the basis of the extracted features. Various parameters, like, FAR, FRR, and accuracy are measured for the execution. Comparative analysis is shown of the proposed and the existing work to depict the effectiveness of the work.

Related Content

Vinod Kumar, Himanshu Prajapati, Sasikala Ponnusamy. © 2023. 18 pages.
Sougatamoy Biswas. © 2023. 14 pages.
Ganga Devi S. V. S.. © 2023. 10 pages.
Gotam Singh Lalotra, Ashok Sharma, Barun Kumar Bhatti, Suresh Singh. © 2023. 15 pages.
Nimish Kumar, Himanshu Verma, Yogesh Kumar Sharma. © 2023. 16 pages.
R. Soujanya, Ravi Mohan Sharma, Manish Manish Maheshwari, Divya Prakash Shrivastava. © 2023. 12 pages.
Nimish Kumar, Himanshu Verma, Yogesh Kumar Sharma. © 2023. 22 pages.
Body Bottom