Stationary Wavelet Transform and Entropy-Based Features for ECG Beat Classification

Simple item page
dc.contributor.authorSaminu, Sani
dc.contributor.authorÖzkurt, Nalan
dc.date.accessioned2022-01-10T11:05:07Z
dc.date.available2022-01-10T11:05:07Z
dc.date.issued2015-07
dc.description.abstractIn this study, heartbeats are classified as normal, right bundle branch block (Rbbb), paced beat, and left bundle branch block (Lbbb), using the electrocardiography (ECG) signals from the MIT-BIH arrhythmia database. The statistical parameters and entropy of stationary wavelet transform (SWT) coefficients are proposed as the features for the classification. The classification was performed using artificial neural networks. It was observed that both statistical parameters and the entropy features perform better than the time domain, and the discrete time wavelet transform coefficient features. The classification performance of the different wavelet families is also considered.en_US
dc.identifier.issn2349-4751
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/7295
dc.language.isoenen_US
dc.publisherIJRSSETen_US
dc.subjectECG beat detectionen_US
dc.subjectcardiac arrhythmiaen_US
dc.subjectstationary wavelet transformen_US
dc.subjectdiscrete wavelet transformen_US
dc.subjectPan Tompkins algorithmen_US
dc.subjectwavelet entropyen_US
dc.titleStationary Wavelet Transform and Entropy-Based Features for ECG Beat Classificationen_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
2J_SS_IJRSSET2015_Stationary Wavelet Transform and Entropy-Based Features for ECG Beat classification.pdf
Size:
445.59 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections