Arrhythmia Classification Using Radial Basis Function Network With Selective Features From Empirical Mode Decomposition

Saumendra Kumar Mohapatra and Mihir Narayan Mohanty
Additional contact information
Saumendra Kumar Mohapatra: ITER, Siksha ‘O' Anusandhan (Deemed), India
Mihir Narayan Mohanty: ITER, Siksha ‘O' Anusandhan (Deemed), India

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2021, vol. 15, issue 1, 39-53

Abstract: In this piece of work, the authors have attempted to classify four types of long duration arrhythmia electrocardiograms (ECG) using radial basis function network (RBFN). The data is taken from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, and features are extracted using empirical mode decomposition (EMD) technique. For most informative contents average power (AP) and coefficient of dispersion (CD) are evaluated from six intrinsic mode function (IMFs) of EMD. Principal component analysis (PCA) is used for feature reduction for effective classification using RBFN. The performance is shown in the result section, and it is found that the classification accuracy is 95.98%.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 18/IJCINI.2021010104 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jcini0:v:15:y:2021:i:1:p:39-53

Access Statistics for this article

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) is currently edited by Kangshun Li

More articles in International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) from IGI Global
Bibliographic data for series maintained by Journal Editor ().