Assessment of the Atrial Fibrillation Burden in Holter ECG Recordings using Artificial Intelligence

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): Swiss National Science Foundation, Swiss Heart Foundation Background Emerging evidence indicates that a high atrial fibrillation (AF) burden is associated with adverse outcome. However, AF burden is not routinely measured in clinical practice. An artificial intelligence (AI)-based tool could facilitate the assessment of AF burden. Purpose We aimed to compare the assessment of AF burden performed manually by physicians with that measured by an AI-based tool. Methods We analysed 7-day Holter ECG recordings of AF patients included in a prospective multicentre cohort study. AF burden was defined as percentage of time in AF, and was assessed manually by physicians and by an AI-based tool. We evaluated the agreement between both techniques by means of Pearson`s correlation coefficient, linear regression model, and Bland-Altman plot. Results We assessed the AF burden in 100 Holter ECG recordings of 82 patients. We identified 53 Holter ECGs with 0% or 100% AF burden, where we found a 100% correlation. For the remaining 47 Holter ECGs with an AF burden between 0.01% and 81,53%, Pearson’s correlation coefficient was 0.998. The calibration intercept was -0.001 (95% CI -0.008; 0.006), and the calibration slope was 0.975 (95% CI 0.954; 0.995; multiple R2 0.995, residual standard error 0.017). Bland-Altman analysis resulted in a bias of -0.006 (95% limits of agreement -0.042 to 0.030). Conclusion The assessment of AF burden with an AI-based tool provides very similar results compared to manual assessment. An AI-based tool may therefore be an accurate and efficient option for the assessment of AF burden.