DOI: 10.1161/circ.148.suppl_1.16400 ISSN: 0009-7322

Abstract 16400: Development of Deep Neural Network Models for Identification of Pulmonary Vein Potentials From Body Surface Electrocardiograms of Patients With Atrial Fibrillation

Munekazu Tanaka, Hirohiko Kohjitani, Tomoyuki Inoue, Akifumi Morinaga, Fumiya Yoneda, Shushi Nishiwaki, Satoshi Shizuta, Yasushi Okuno, Koh Ono
  • Physiology (medical)
  • Cardiology and Cardiovascular Medicine

[Background] Pulmonary vein (PV) potential is essential as the origin of atrial fibrillation (AF). However, no established method exists to evaluate PV potentials on a body surface ECG (BS-ECG).

[Objective] To develop a method to identify PV potentials from BS-ECGs using state-of-the-art deep neural network (DNN) technology.

[Methods and results] A cohort of 799 consecutive paroxysmal AF patients, who underwent Pulmonary Vein Isolation (PVI), were enrolled, and pre- and post-PVI 12-lead ECGs (12ECGs) were collected, amounting to a dataset of 1598 ECGs. Four DNN models were constructed, each employing distinct architectures: 1D-ResNet, 1D-DenseNet, 1D-EfficientNet-b3, and 1D-ViT (Vision Transformer). These models were rigorously trained and evaluated for the identification of PV potentials. The 1D-EfficientNet-b3-based model exhibited optimal performance, achieving an accuracy of 89.4% in the training set and 89.1% in the test set. The Gradient-weighted Class Activation Mapping (Grad-CAM) effectively demonstrated that the segments 200-300ms before the P-wave is crucial for identifying PV potentials. Furthermore, DNN models were developed for each single-lead ECG. While there were variations in the accuracy of PV potential identification among single-lead models, the V6-lead model demonstrated superior performance with an accuracy of 83.2%.

[Conclusions] The findings of this research strongly suggest that our DNN-based model can accurately differentiate PV potentials from BS-ECGs. Additionally, Grad-CAM visualizations indicate that pertinent features for the identification of PV potentials are predominantly clustered in the P-wave frontal region. This signifies a promising advancement in the non-invasive detection and analysis of PV potentials, paving the way for improved management of paroxysmal AF.

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