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      Detection of atrial fibrillation with a smartphone camera: first prospective, international, two-centre, clinical validation study (DETECT AF PRO)

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          Abstract

          Aims

          Early detection of atrial fibrillation (AF) is essential for stroke prevention. Emerging technologies such as smartphone cameras using photoplethysmography (PPG) and mobile, internet-enabled electrocardiography (iECG) are effective for AF screening. This study compared a PPG-based algorithm against a cardiologist’s iECG diagnosis to distinguish between AF and sinus rhythm (SR).

          Methods and results

          In this prospective, two-centre, international, clinical validation study, we recruited in-house patients with presumed AF and matched controls in SR at two university hospitals in Switzerland and Germany. In each patient, a PPG recording on the index fingertip using a regular smartphone camera followed by iECG was obtained. Photoplethysmography recordings were analysed using an automated algorithm and compared with the blinded cardiologist’s iECG diagnosis. Of 672 patients recruited, 80 were excluded mainly due to insufficient PPG/iECG quality, leaving 592 patients (SR: n = 344, AF: n = 248). Based on 5 min of PPG heart rhythm analysis, the algorithm detected AF with a sensitivity of 91.5% (95% confidence interval 85.9–95.4) and specificity of 99.6% (97.8–100). By reducing analysis time to 1 min, sensitivity was reduced to 89.9% (85.5–93.4) and specificity to 99.1% (97.5–99.8). Correctly classified rate was 88.8% for 1-min PPG analysis and dropped to 60.9% when the threshold for the analysed file was set to 5 min of good signal quality.

          Conclusion

          This is the first prospective clinical two-centre study to demonstrate that detection of AF by using a smartphone camera alone is feasible, with high specificity and sensitivity. Photoplethysmography signal analysis appears to be suitable for extended AF screening.

          Clinical trial registration

          ClinicalTrials.gov, number NCT02949180, https://clinicaltrials.gov/ct2/show/NCT02949180.

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          Most cited references9

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          Screening for Atrial Fibrillation: A Report of the AF-SCREEN International Collaboration.

          Approximately 10% of ischemic strokes are associated with atrial fibrillation (AF) first diagnosed at the time of stroke. Detecting asymptomatic AF would provide an opportunity to prevent these strokes by instituting appropriate anticoagulation. The AF-SCREEN international collaboration was formed in September 2015 to promote discussion and research about AF screening as a strategy to reduce stroke and death and to provide advocacy for implementation of country-specific AF screening programs. During 2016, 60 expert members of AF-SCREEN, including physicians, nurses, allied health professionals, health economists, and patient advocates, were invited to prepare sections of a draft document. In August 2016, 51 members met in Rome to discuss the draft document and consider the key points arising from it using a Delphi process. These key points emphasize that screen-detected AF found at a single timepoint or by intermittent ECG recordings over 2 weeks is not a benign condition and, with additional stroke factors, carries sufficient risk of stroke to justify consideration of anticoagulation. With regard to the methods of mass screening, handheld ECG devices have the advantage of providing a verifiable ECG trace that guidelines require for AF diagnosis and would therefore be preferred as screening tools. Certain patient groups, such as those with recent embolic stroke of uncertain source (ESUS), require more intensive monitoring for AF. Settings for screening include various venues in both the community and the clinic, but they must be linked to a pathway for appropriate diagnosis and management for screening to be effective. It is recognized that health resources vary widely between countries and health systems, so the setting for AF screening should be both country- and health system-specific. Based on current knowledge, this white paper provides a strong case for AF screening now while recognizing that large randomized outcomes studies would be helpful to strengthen the evidence base.
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            Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch

            Question How well can smartwatch sensor data analyzed by a deep neural network identify atrial fibrillation? Findings In this cohort study of 51 participants presenting for cardioversion, a commercially available smartwatch was able to detect atrial fibrillation with high accuracy. Among 1617 ambulatory individuals who wore a smartwatch, those with self-reported atrial fibrillation were correctly classified with moderate accuracy. Meaning These data support the proof of concept that a commercially available smartwatch coupled with a deep neural network classifier can passively detect atrial fibrillation. This study aims to develop and validate a deep neural network to detect atrial fibrillation using smartwatch data. Importance Atrial fibrillation (AF) affects 34 million people worldwide and is a leading cause of stroke. A readily accessible means to continuously monitor for AF could prevent large numbers of strokes and death. Objective To develop and validate a deep neural network to detect AF using smartwatch data. Design, Setting, and Participants In this multinational cardiovascular remote cohort study coordinated at the University of California, San Francisco, smartwatches were used to obtain heart rate and step count data for algorithm development. A total of 9750 participants enrolled in the Health eHeart Study and 51 patients undergoing cardioversion at the University of California, San Francisco, were enrolled between February 2016 and March 2017. A deep neural network was trained using a method called heuristic pretraining in which the network approximated representations of the R-R interval (ie, time between heartbeats) without manual labeling of training data. Validation was performed against the reference standard 12-lead electrocardiography (ECG) in a separate cohort of patients undergoing cardioversion. A second exploratory validation was performed using smartwatch data from ambulatory individuals against the reference standard of self-reported history of persistent AF. Data were analyzed from March 2017 to September 2017. Main Outcomes and Measures The sensitivity, specificity, and receiver operating characteristic C statistic for the algorithm to detect AF were generated based on the reference standard of 12-lead ECG–diagnosed AF. Results Of the 9750 participants enrolled in the remote cohort, including 347 participants with AF, 6143 (63.0%) were male, and the mean (SD) age was 42 (12) years. There were more than 139 million heart rate measurements on which the deep neural network was trained. The deep neural network exhibited a C statistic of 0.97 (95% CI, 0.94-1.00; P  < .001) to detect AF against the reference standard 12-lead ECG–diagnosed AF in the external validation cohort of 51 patients undergoing cardioversion; sensitivity was 98.0% and specificity was 90.2%. In an exploratory analysis relying on self-report of persistent AF in ambulatory participants, the C statistic was 0.72 (95% CI, 0.64-0.78); sensitivity was 67.7% and specificity was 67.6%. Conclusions and Relevance This proof-of-concept study found that smartwatch photoplethysmography coupled with a deep neural network can passively detect AF but with some loss of sensitivity and specificity against a criterion-standard ECG. Further studies will help identify the optimal role for smartwatch-guided rhythm assessment.
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              The optics of human skin.

              An integrated review of the transfer of optical radiation into human skin is presented, aimed at developing useful models for photomedicine. The component chromophores of epidermis and stratum corneum in general determine the attenuation of radiation in these layers, moreso than does optical scattering. Epidermal thickness and melanization are important factors for UV wavelengths less than 300 nm, whereas the attenuation of UVA (320-400 nm) and visible radiation is primarily via melanin. The selective penetration of all optical wavelengths into psoriatic skin can be maximized by application of clear lipophilic liquids, which decrease regular reflectance by a refractive-index matching mechanism. Sensitivity to wavelengths less than 320 nm can be enhanced by prolonged aqueous bathing, which extracts urocanic acid and other diffusible epidermal chromophores. Optical properties of the dermis are modelled using the Kubelka-Munk approach, and calculations of scattering and absorption coefficients are presented. This simple approach allows estimates of the penetration of radiation in vivo using noninvasive measurements of cutaneous spectral remittance (diffuse reflectance). Although the blood chromophores Hb, HbO2, and bilirubin determine dermal absorption of wavelengths longer than 320 nm, scattering by collagen fibers largely determines the depths to which these wavelengths penetrate the dermis, and profoundly modifies skin colors. An optical "window" exists between 600 and 1300 nm, which offers the possibility of treating large tissue volumes with certain long-wavelength photosensitizers. Moreover, whenever photosensitized action spectra extend across the near UV and/or visible spectrum, judicious choice of wavelengths allows some selection of the tissue layers directly affected.
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                Author and article information

                Journal
                Europace
                Europace
                europace
                Europace
                Oxford University Press
                1099-5129
                1532-2092
                January 2019
                31 July 2018
                31 July 2018
                : 21
                : 1
                : 41-47
                Affiliations
                [1 ]CMIO office, University Hospital Basel, Petersgraben 4, Basel, Switzerland
                [2 ]Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
                [3 ]German Centre for Cardiovascular Research (DZHK), partner site Greifswald, Greifswald, Germany
                [4 ]Department of Internal Medicine, University Hospital Regensburg, Regensburg, Germany
                [5 ]Herzklinik Ulm, Ulm, Germany
                [6 ]Department of Internal Medicine, University Hospital Basel, Petersgraben 4, Basel, Switzerland
                Author notes
                Corresponding author. Tel: +41 61 328 7689; fax: +41 61 265 5353. E-mail address: jens.eckstein@ 123456usb.ch

                Noé Brasier and Christina J. Raichle authors contributed equally to this work.

                Article
                euy176
                10.1093/europace/euy176
                6321964
                30085018
                f5aaae9b-f1be-4d13-96d5-1ee3125ce4c8
                © The Author(s) 2018. Published by Oxford University Press on behalf of the European Society of Cardiology

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 20 April 2018
                : 25 July 2018
                Page count
                Pages: 7
                Funding
                Funded by: University Hospital Basel
                Categories
                Clinical Research
                Atrial Fibrillation
                Editor's Choice

                Cardiovascular Medicine
                atrial fibrillation,arrhythmia,photoplethysmography,smartphone,ehealth
                Cardiovascular Medicine
                atrial fibrillation, arrhythmia, photoplethysmography, smartphone, ehealth

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