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.

Identification of Morphological Patterns for the Detection of Premature Ventricular Contractions

De Marco, F;Di Biasi, L;Auriemma Citarella, A;Tucci, M;Tortora, G
2022-01-01

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.
2022
978-1-6654-9007-8
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4853514
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