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@article{ART002226621},
author={Kyeong-Seop Kim},
title={Assessment of Premature Ventricular Contraction Arrhythmia by K-means Clustering Algorithm},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2017},
volume={22},
number={5},
pages={65-72},
doi={10.9708/jksci.2017.22.05.065}

TY - JOUR
AU - Kyeong-Seop Kim
TI - Assessment of Premature Ventricular Contraction Arrhythmia by K-means Clustering Algorithm
JO - Journal of The Korea Society of Computer and Information
PY - 2017
VL - 22
IS - 5
PB - The Korean Society Of Computer And Information
SP - 65
EP - 72
SN - 1598-849X
AB - Premature Ventricular Contraction(PVC) arrhythmia is most common abnormal-heart rhythm that may increase mortal risk of a cardiac patient. Thus, it is very important issue to identify the specular portraits of PVC pattern especially from the patient. In this paper, we propose a new method to extract the characteristics of PVC pattern by applying K-means machine learning algorithm on Heart Rate Variability depicted in Poinecare plot. For the quantitative analysis to distinguish the trend of cluster patterns between normal sinus rhythm and PVC beat, the Euclidean distance measure was sought between the clusters. Experimental simulations on MIT-BIH arrhythmia database draw the fact that the distance measure on the cluster is valid for differentiating the pattern-traits of PVC beats. Therefore, we proposed a method that can offer the simple remedy to identify the attributes of PVC beats in terms of K-means clusters especially in the long-period Electrocardiogram(ECG).
KW - Electrocardiogram;Premature Ventricular Contraction;K-Means Clustering;Poincare Plot;Heart Rate Variability
DO - 10.9708/jksci.2017.22.05.065
ER -

Kyeong-Seop Kim. (2017). Assessment of Premature Ventricular Contraction Arrhythmia by K-means Clustering Algorithm. Journal of The Korea Society of Computer and Information, 22(5), 65-72.

Kyeong-Seop Kim. 2017, "Assessment of Premature Ventricular Contraction Arrhythmia by K-means Clustering Algorithm", Journal of The Korea Society of Computer and Information, vol.22, no.5 pp.65-72. Available from: doi:10.9708/jksci.2017.22.05.065

Kyeong-Seop Kim "Assessment of Premature Ventricular Contraction Arrhythmia by K-means Clustering Algorithm" Journal of The Korea Society of Computer and Information 22.5 pp.65-72 (2017) : 65.

Kyeong-Seop Kim. Assessment of Premature Ventricular Contraction Arrhythmia by K-means Clustering Algorithm. 2017; 22(5), 65-72. Available from: doi:10.9708/jksci.2017.22.05.065

Kyeong-Seop Kim. "Assessment of Premature Ventricular Contraction Arrhythmia by K-means Clustering Algorithm" Journal of The Korea Society of Computer and Information 22, no.5 (2017) : 65-72.doi: 10.9708/jksci.2017.22.05.065

Kyeong-Seop Kim. Assessment of Premature Ventricular Contraction Arrhythmia by K-means Clustering Algorithm. Journal of The Korea Society of Computer and Information, 22(5), 65-72. doi: 10.9708/jksci.2017.22.05.065

Kyeong-Seop Kim. Assessment of Premature Ventricular Contraction Arrhythmia by K-means Clustering Algorithm. Journal of The Korea Society of Computer and Information. 2017; 22(5) 65-72. doi: 10.9708/jksci.2017.22.05.065

Kyeong-Seop Kim. Assessment of Premature Ventricular Contraction Arrhythmia by K-means Clustering Algorithm. 2017; 22(5), 65-72. Available from: doi:10.9708/jksci.2017.22.05.065

Kyeong-Seop Kim. "Assessment of Premature Ventricular Contraction Arrhythmia by K-means Clustering Algorithm" Journal of The Korea Society of Computer and Information 22, no.5 (2017) : 65-72.doi: 10.9708/jksci.2017.22.05.065