Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2024
Enhancing origin prediction: deep learning model for diagnosing premature ventricular contractions with dual-rhythm analysis focused on cardiac rotation.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Nakasone Kazutaka, Nishimori Makoto, Shinohara Masakazu, Takami Mitsuru, Imamura Kimitake, Nishida Taku, Shimane Akira, Oginosawa Yasushi, Nakamura Yuki, Yamauchi Yasuteru, Fujiwara Ryudo, Asada Hiroyuki, Yoshida Akihiro, Takami Kaoru, Akita Tomomi, Nagai Takayuki, Sommer Philipp, El Hamriti Mustapha, Imada Hiroshi, Pannone Luigi, Sarkozy Andrea, Chierchia Gian Battista, de Asmundis Carlo, Kiuchi Kunihiko, Hirata Ken-Ichi, Fukuzawa Koji
Аннотация
AIMS: Several algorithms can differentiate inferior axis premature ventricular contractions (PVCs) originating from the right side and left side on 12-lead electrocardiograms (ECGs). However, it is unclear whether distinguishing the origin should rely solely on PVC or incorporate sinus rhythm (SR). We compared the dual-rhythm model (incorporating both SR and PVC) to the PVC model (using PVC alone) and quantified the contribution of each ECG lead in predicting the PVC origin for each cardiac rotation.
METHODS AND RESULTS: This multicentre study enrolled 593 patients from 11 centres-493 from Japan and Germany, and 100 from Belgium, which were used as the external validation data set. Using a hybrid approach combining a Resnet50-based convolutional neural network and a transformer model, we developed two variants-the PVC and dual-rhythm models-to predict PVC origin. In the external validation data set, the dual-rhythm model outperformed the PVC model in accuracy (0.84 vs. 0.74, respectively; P < 0.01), precision (0.73 vs. 0.55, respectively; P < 0.01), specificity (0.87 vs. 0.68, respectively; P < 0.01), area under the receiver operating characteristic curve (0.91 vs. 0.86, respectively; P = 0.03), and F1-score (0.77 vs. 0.68, respectively; P = 0.03). The contributions to PVC origin prediction were 77.3% for PVC and 22.7% for the SR. However, in patients with counterclockwise rotation, SR had a greater contribution in predicting the origin of right-sided PVC.
CONCLUSION: Our deep learning-based model, incorporating both PVC and SR morphologies, resulted in a higher prediction accuracy for PVC origin, considering SR is particularly important for predicting right-sided origin in patients with counterclockwise rotation.
METHODS AND RESULTS: This multicentre study enrolled 593 patients from 11 centres-493 from Japan and Germany, and 100 from Belgium, which were used as the external validation data set. Using a hybrid approach combining a Resnet50-based convolutional neural network and a transformer model, we developed two variants-the PVC and dual-rhythm models-to predict PVC origin. In the external validation data set, the dual-rhythm model outperformed the PVC model in accuracy (0.84 vs. 0.74, respectively; P < 0.01), precision (0.73 vs. 0.55, respectively; P < 0.01), specificity (0.87 vs. 0.68, respectively; P < 0.01), area under the receiver operating characteristic curve (0.91 vs. 0.86, respectively; P = 0.03), and F1-score (0.77 vs. 0.68, respectively; P = 0.03). The contributions to PVC origin prediction were 77.3% for PVC and 22.7% for the SR. However, in patients with counterclockwise rotation, SR had a greater contribution in predicting the origin of right-sided PVC.
CONCLUSION: Our deep learning-based model, incorporating both PVC and SR morphologies, resulted in a higher prediction accuracy for PVC origin, considering SR is particularly important for predicting right-sided origin in patients with counterclockwise rotation.