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Updated: Jul 21, 2025

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
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A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG

Bader Aldughayfiq1, Farzeen Ashfaq2, N Z Jhanjhi2

  • 1Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|July 29, 2023
PubMed
Summary

This study developed a deep learning model using Photoplethysmogram (PPG) signals to accurately detect atrial fibrillation (AF). The hybrid 1D CNN and BiLSTM network achieved 95% accuracy, offering a promising non-invasive AF classification method.

Keywords:
1D convolution neural networksBiLSTM networkatrial fibrillationdeep learningelectrocardiogramphotoplethysmogram

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