Krivoshei, Lian and Weber, Stefan and Burkard, Thilo and Maseli, Anna and Brasier, Noe and Kuhne, Michael and Conen, David and Huebner, Thomas and Seeck, Andrea and Eckstein, Jens (2017) Smart detection of atrial fibrillation. EUROPACE, 19 (5). pp. 753-757. ISSN 1099-5129, 1532-2092
Full text not available from this repository. (Request a copy)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 Poincare 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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | HEART-RATE-VARIABILITY; CRYPTOGENIC STROKE; POINCARE PLOT; FAILURE; fibrillation; Pulse wave analysis; Rhythm monitoring; Smartphone |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Innere Medizin I |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 14 Dec 2018 13:10 |
| Last Modified: | 20 Feb 2019 14:48 |
| URI: | https://pred.uni-regensburg.de/id/eprint/907 |
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