Unsupervised Classification of Premature Ventricular Contractions Based on RR Interval and Heartbeat Morphology

4Citations
Citations of this article
6Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Accurate automated detection of premature ventricular contractions from electrocardiogram requires a training set or expert intervention. We propose a fully automated unsupervised detection method. The algorithm first clusters morphologically similar heartbeats and then performs classification based on RR intervals and morphology. Tests on clinically recorded datasets show sensitivity of 94.7%, specificity of 99.6% and accuracy of 99.5%.

Cite

CITATION STYLE

APA

Atanasoski, V., Ivanovic, M. D., Marinkovic, M., Gligoric, G., Bojovic, B., Shvilkin, A. V., & Petrovic, J. (2018). Unsupervised Classification of Premature Ventricular Contractions Based on RR Interval and Heartbeat Morphology. In 2018 14th Symposium on Neural Networks and Applications, NEUREL 2018. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/NEUREL.2018.8586997

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free