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Robust deep learning pipeline for PVC beats localization.

Bohdan Petryshak¹, Illia Kachko¹, Mykola Maksymenko²

¹The Machine Learning Laboratory, Ukrainian Catholic University Lviv, Ukraine.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine

|March 8, 2021

Summary

This summary is machine-generated.

This study introduces a novel framework for automated Premature Ventricular Contraction (PVC) detection using two neural networks. The method accurately localizes R peaks and classifies PVC beats, outperforming traditional algorithms.

Keywords:

ECG classification ECG segmentation Electrocardiography PVC identification

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Area of Science:

Cardiology
Biomedical Engineering
Artificial Intelligence

Background:

Premature Ventricular Contraction (PVC) is a common arrhythmia.
Current automated PVC detection methods face limitations with feature engineering and small datasets.

Purpose of the Study:

To develop a robust framework for automated PVC identification and R-peak localization.
To overcome the limitations of existing methods in handling PVC beats.

Main Methods:

A two-stage neural network approach: an encoder-decoder for R-peak localization and CardioIncNet for beat classification.
Training on a PVC-rich dataset for accurate localization of normal and anomalous heartbeats.

Main Results:

Achieved F1-measures over 0.99 for R-peak localization and over 0.96 for PVC beat classification in single-dataset evaluations.
Demonstrated strong performance (F1 > 0.979 and > 0.85) in cross-dataset evaluations.

Conclusions:

The proposed method offers robust performance for PVC detection, surpassing classical algorithms.
The framework provides reliable results across diverse datasets and is available for reproduction.