Circulation journal : official journal of the Japanese Circulation Society · 2025
Deep Learning Model for High-Accuracy Classification of Premature Ventricular Contractions With Precordial Transition Zones in Leads V3 or V4.
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Miyamae Kiichi, Inden Yasuya, Shimojo Masafumi, Miyazawa Hiroyuki, Iwawaki Tomoya, Tachi Masaya, Kondo Shun, Goto Takayuki, Yanagisawa Satoshi, Tsuji Yukiomi, Murohara Toyoaki
Аннотация
BACKGROUND: Predicting the origin of premature ventricular contractions (PVCs) is challenging when a transition zone (TZ) appears in leads V3 and V4. The aim of this study was to develop a deep-learning model to predict PVC origins and identify electrocardiographic (ECG) features that contribute to the model's decisions.
METHODS AND RESULTS: ECG data from 314 patients with PVCs showing an inferior axis and TZ in leads V3 or V4 who underwent catheter ablation were analyzed. A convolutional neural network (CNN) was trained to predict an origin in the right or left ventricular outflow tract. Patients were divided into 3 cohorts for training, validation, and holdout (3 : 1 : 1 ratio). The CNN model was trained using paired data consisting of PVC and intrinsic QRS (iQRS). Five datasets per patient were used for training and validation; performance was evaluated using a single holdout dataset per patient. The CNN model achieved 92.1% accuracy, an F1 score of 0.91, and an area under the receiver operating characteristic curve of 0.96 on the holdout. Our model demonstrated superior diagnostic performance compared with conventional ECG indices. Gradient-weighted class activation mapping revealed that model attention was primarily focused on leads V3-V4 in iQRS, but was more diffusely distributed in PVC, notably the inferior limb leads and leads V2-V3.
CONCLUSIONS: The CNN-based prediction of PVC origin demonstrated clinical utility.
METHODS AND RESULTS: ECG data from 314 patients with PVCs showing an inferior axis and TZ in leads V3 or V4 who underwent catheter ablation were analyzed. A convolutional neural network (CNN) was trained to predict an origin in the right or left ventricular outflow tract. Patients were divided into 3 cohorts for training, validation, and holdout (3 : 1 : 1 ratio). The CNN model was trained using paired data consisting of PVC and intrinsic QRS (iQRS). Five datasets per patient were used for training and validation; performance was evaluated using a single holdout dataset per patient. The CNN model achieved 92.1% accuracy, an F1 score of 0.91, and an area under the receiver operating characteristic curve of 0.96 on the holdout. Our model demonstrated superior diagnostic performance compared with conventional ECG indices. Gradient-weighted class activation mapping revealed that model attention was primarily focused on leads V3-V4 in iQRS, but was more diffusely distributed in PVC, notably the inferior limb leads and leads V2-V3.
CONCLUSIONS: The CNN-based prediction of PVC origin demonstrated clinical utility.