(untitled) (Vietnam Journal of Community Medicine, 2025)
Objectives: To develop an AI model capable of accurately detecting PVCs and to evaluate its screening performance on reference standardized ECG datasets. Methods: This retrospective study utilized 24-hour Holter ECG data collected from Nguyen Trai Hospital and Nguyen Tri Phuong Hospital between 2021 and 2024. Data labeling and analysis were performed from October 2024 to April 2025. The AI model was constructed using a deep learning-based ResNet architecture. Results: From a total of 453 Holter ECG datasets, 643675 PVCs were identified, with the rate of patients exhibiting frequent PVCs recorded as 4.0%. The prevalence of PVC couplet, bigeminy, and trigeminy was 17.0%, 31.8%, and 29.1%, respectively. The developed AI model demonstrated a sensitivity of over 80%, a specificity exceeding 90%, and an F1-score above 85% when validated against MIT-BIH, AHA, and ESC reference datasets. Conclusion: Our AI model has strong potential for real-world application in large-scale ECG-based PVC screening, offering an efficient and scalable solution for PVC detection