학술논문

Identification of Morphological Patterns for the Detection of Premature Ventricular Contractions
Document Type
Conference
Source
2022 26th International Conference Information Visualisation (IV) IV Information Visualisation (IV), 2022 26th International Conference. :393-398 Jul, 2022
Subject
Computing and Processing
Heart
Pathology
Heart beat
Arrhythmia
Clustering algorithms
Electrocardiography
Classification algorithms
Detection
Pattern
Convolutional Neural Networks
Cluster
Electrocardiogram
Premature Ventricular Contraction
Language
ISSN
2375-0138
Abstract
Premature ventricular contractions (PVCs) are abnormal heartbeats that begin in the lower ventricles or pumping chambers and disrupt the normal heart rhythm. The electrocardiogram (ECG) is the most often used tool for detecting abnormalities in the heart's electrical activity. PVCs are very frequent and usually harmless, but they can be extremely harmful in patients with significant heart problems. As a result, appropriate prevention combined with adequate treatment can improve patients' lives. This paper presents preliminary results on the main challenge associated with the detection of PVCs: identifying common patterns. The images used were extrapolated from the MIT-BIH Arrhythmia Database and then pre-processed to remove any signal noise before creating a distance matrix based on the wave distances of each pair of analyzed images. Finally, we clustered the distance into four groups using clustering algorithms such as K-means. We used a graph-based structure to graphically represent and explore cluster elements in this work. Preliminary results suggest the presence of four distinct patterns.