Automated Classification of Atrioventricular Block Based on Semantic Segmentation of ECG Signals.

Jong Doo Choi; Eunsol Soul Choi; Eunyoung Ro; Sung Jin Ahn; Hoo Hyun Kim; Gyung Chul Kim

doi:10.3233/shti251210

August 7, 2025

Automated Classification of Atrioventricular Block Based on Semantic Segmentation of ECG Signals.

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Key Points

The algorithm accurately detects P-waves, a critical component in identifying atrioventricular block.
Semantic segmentation techniques are applied to improve the classification accuracy of ECG signals.
A single-channel wearable ECG device is used for easy attachment and monitoring of heart activity.
This classification method may advance telemedicine by enabling remote monitoring of cardiac conditions.

Implication

Algorithm detects P-waves and classifies atrioventricular block in ECG signals, suggesting enhancements in wearable technology.

Abstract

We developed an algorithm that accurately detects P-waves and classifies atrioventricular block (AVB) by applying a semantic segmentation algorithm to an easily attachable single-channel wearable electrocardiogram (ECG).

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Cite This Study

Choi et al. (2025) studied this question.

synapsesocial.com/papers/689dfe97d61984b91e13c058 https://doi.org/10.3233/shti251210

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Automated Classification of Atrioventricular Block Based on Semantic Segmentation of ECG Signals.

Key Points

Implication

Abstract

Discussion

Cite This Study

Automated Classification of Atrioventricular Block Based on Semantic Segmentation of ECG Signals.

Key Points

Implication

Abstract

Discussion

Cite This Study

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