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. 2023 Jan 27;4(2):41-47.
doi: 10.1016/j.cvdhj.2023.01.003. eCollection 2023 Apr.

Assessment of the atrial fibrillation burden in Holter electrocardiogram recordings using artificial intelligence

Elisa Hennings  1   2 , Michael Coslovsky  3 , Rebecca E Paladini  1   2 , Stefanie Aeschbacher  1   2 , Sven Knecht  1   2 , Vincent Schlageter  1 , Philipp Krisai  1   2 , Patrick Badertscher  1   2 , Christian Sticherling  1   2 , Stefan Osswald  1   2 , Michael Kühne  1   2 , Christine S Zuern  1   2 ; Swiss-AF Investigators
Affiliations

Assessment of the atrial fibrillation burden in Holter electrocardiogram recordings using artificial intelligence

Elisa Hennings et al. Cardiovasc Digit Health J. .

Abstract

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.

Objective: We aimed to compare the assessment of AF burden performed manually by physicians with that measured by an AI-based tool.

Methods: We analyzed 7-day Holter electrocardiogram (ECG) recordings of AF patients included in the prospective, multicenter Swiss-AF Burden cohort study. AF burden was defined as percentage of time in AF, and was assessed manually by physicians and by an AI-based tool (Cardiomatics, Cracow, Poland). We evaluated the agreement between both techniques by means of Pearson 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 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 provided 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.

Keywords: Artificial intelligence; Atrial fibrillation; Atrial fibrillation burden; Deep learning; Holter ECG; Machine learning.

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Figures

None
AF = atrial fibrillation; AI = artificial intelligence; ECG = electrocardiogram.
Figure 1
Figure 1
Flowchart of study population and Holter electrocardiograms (ECGs). AF = atrial fibrillation. ∗Holter ECGs with an AF burden of 0% or 100% (where a 100% correlation between manual and artificial intelligence (AI)–measured AF burden was detected) were not used to assess the performance of the AI-based tool to detect AF burden.
Figure 2
Figure 2
Violin and box plots of atrial fibrillation (AF) burden assessed manually and by artificial intelligence (AI)–based tool. Red cross indicates mean.
Figure 3
Figure 3
Calibration plot of atrial fibrillation (AF) burden assessed manually and by artificial intelligence (AI)-based tool. Black line: 1 to 1 line; red line: linear regression line with calibration intercept = -0.001 (95% CI -0.008; 0.006) and calibration slope = 0.975 (95% CI 0.954; 0.995).
Figure 4
Figure 4
Bland-Altman plot of atrial fibrillation (AF) burden assessed manually and by artificial intelligence (AI)-based tool. Bias of -0.006 (red line) with 95% limits of agreement of -0.042 (95% CI -0.051; -0.033) to 0.030 (95% CI 0.020; 0.039) (dotted black lines). X-axis = mean of AF burden measurements, Y-axis = difference of the means of AF burden measurements assessed manually and by AI-based tool.
See this image and copyright information in PMC

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