Performance of an automated photoplethysmography-based artificial intelligence algorithm to detect atrial fibrillation

Daniel Mol, Robert K Riezebos, Henk A Marquering, Marije E Werner, Trudie C A Lobban, Jonas S S G de Jong, Joris R de Groot, Daniel Mol, Robert K Riezebos, Henk A Marquering, Marije E Werner, Trudie C A Lobban, Jonas S S G de Jong, Joris R de Groot

No abstract available

Keywords: Artificial intelligence; Atrial fibrillation; Photoplethysmography; Smartphone.

Figures

Figure 1
Figure 1
Peak detection errors (%) during sinus rhythm (SR) (x-axis) and atrial fibrillation (AF) (y-axis) using MATLAB findpeaks (ML) (blue dots) and shallow neural network (SNN) (red dot). Errors included false and missed peaks.
Figure 2
Figure 2
Steps taken by the photoplethysmography (PPG) algorithm to provide heart rhythm outcomes. First is detection of peaks using a shallow neural network; second is quality estimation using the support vector machine. After selection of 3 segments with the best quality in the third step, each segment is, based on rhythm features, classified as sinus rhythm (SR), atrial fibrillation (AF), or undetermined (UD). The final decision was made if ≥2 segments were classified in the same group.
Figure 3
Figure 3
Flow diagram validation study. ECG = electrocardiography; ECV = electrical cardioversion.
Figure 4
Figure 4
Signal quality and 2×2 tables. A: Example of high, medium, and low signal quality plethysmography recordings during atrial fibrillation (AF). B: 2× 2 tables with algorithm outcome. Top table does not include recordings classified as undetermined (sensitivity 98.1%, 95% confidence interval [CI] 93.4%–99.8%; specificity 98.1%, 95% CI 93.2%–99.8%). In the bottom table, undetermined recordings are classified as false positive or false negative (sensitivity 96.3%, 95% CI 90.8%–99.0%; specificity 93.5%, 95% CI 87.1%–97.4%). SR = sinus rhythm.

References

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Source: PubMed

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