Machine learning based potentiating impacts of 12‑lead ECG for classifying paroxysmal versus non‑paroxysmal atrial fibrillation
10.1186/s42444-022-00061-3
- Author:
Sungsoo KIM
1
;
Sohee KWON
;
Mia K. MARKEY
;
Alan C. BOVIK
;
Sung‑Hwi HONG
;
JunYong KIM
;
Hye Jin HWANG
;
Boyoung JOUNG
;
Hui‑Nam PAK
;
Moon‑Hyeong LEE
;
Junbeom PARK
Author Information
1. Electrical and Computer Engineering, The University of Texas at Austin, Austin, USA
- Publication Type:RESEARCH
- From:International Journal of Arrhythmia
2022;23(2):11-
- CountryRepublic of Korea
- Language:English
-
Abstract:
Background:Conventional modality requires several days observation by Holter monitor to differentiate atrial fibril‑ lation (AF) between Paroxysmal atrial fibrillation (PAF) and Non-paroxysmal atrial fibrillation (Non-PAF). Rapid and practical differentiating approach is needed.
Objective:To develop a machine learning model that observes 10-s of standard 12-lead electrocardiograph (ECG) for real-time classification of AF between PAF versus Non-PAF.
Methods:In this multicenter, retrospective cohort study, the model training and cross-validation was performed on a dataset consisting of 741 patients enrolled from Severance Hospital, South Korea. For cross-institutional validation, the trained model was applied to an independent data set of 600 patients enrolled from Ewha University Hospital, South Korea. Lasso regression was applied to develop the model.
Results:In the primary analysis, the Area Under the Receiver Operating Characteristic Curve (AUC) on the test set for the model that predicted AF subtype only using ECG was 0.72 (95% CI 0.65–0.80). In the secondary analysis, AUC only using baseline characteristics was 0.53 (95% CI 0.45–0.61), while the model that employed both baseline characteris‑ tics and ECG parameters was 0.72 (95% CI 0.65–0.80). Moreover, the model that incorporated baseline characteristics, ECG, and Echocardiographic parameters achieved an AUC of 0.76 (95% CI 0.678–0.855) on the test set.
Conclusions:Our machine learning model using ECG has potential for automatic differentiation of AF between PAF versus Non-PAF achieving high accuracy. The inclusion of Echocardiographic parameters further increases model per‑ formance. Further studies are needed to clarify the next steps towards clinical translation of the proposed algorithm.
