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. 2017 May 1;19(5):753-757.
doi: 10.1093/europace/euw125.

Smart detection of atrial fibrillation†

Lian Krivoshei  1   2 , Stefan Weber  3 , Thilo Burkard  4 , Anna Maseli  1 , Noe Brasier  1 , Michael Kühne  5 , David Conen  1 , Thomas Huebner  6 , Andrea Seeck  6 , Jens Eckstein  1
Affiliations

Smart detection of atrial fibrillation†

Lian Krivoshei et al. Europace. .

Abstract

Aims: Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice, and its paroxysmal nature makes its detection challenging. In this trial, we evaluated a novel App for its accuracy to differentiate between patients in AF and patients in sinus rhythm (SR) using the plethysmographic sensor of an iPhone 4S and the integrated LED only.

Methods and results: For signal acquisition, we used an iPhone 4S, positioned with the camera lens and LED light on the index fingertip. A 5 min video file was recorded with the pulse wave extracted from the green light spectrum of the signal. RR intervals were automatically identified. For discrimination between AF and SR, we tested three different statistical methods. Normalized root mean square of successive difference of RR intervals (nRMSSD), Shannon entropy (ShE), and SD1/SD2 index extracted from a Poincaré plot. Eighty patients were included in the study (40 patients in AF and 40 patients in SR at the time of examination). For discrimination between AF and SR, ShE yielded the highest sensitivity and specificity with 85 and 95%, respectively. Applying a tachogram filter resulted in an improved sensitivity of 87.5%, when combining ShE and nRMSSD, while specificity remained stable at 95%. A combination of SD1/SD2 index and nRMSSD led to further improvement and resulted in a sensitivity and specificity of 95%.

Conclusion: The algorithm tested reliably discriminated between SR and AF based on pulse wave signals from a smartphone camera only. Implementation of this algorithm into a smartwatch is the next logical step.

Keywords: Atrial fibrillation; Pulse wave analysis; Rhythm monitoring; Smartphone.

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Figures

Figure 1
Figure 1
iPhone on index finger tip with resulting pulse wave signal of a patient with AF.
Figure 2
Figure 2
Poincaré plots of 5 min recordings from patients in SR (A) and patients in AF (B).
Figure 3
Figure 3
Comparison of nRMSSD (A), ShE (B), and SD1/SD2 (C) in patients with SR and AF.
Figure 4
Figure 4
Area under the curve for Test 3, which combined SD1/SD2 analysed from the Poincaré plot and nRMSSD.
See this image and copyright information in PMC

References

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