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

Brasier, Noé; Raichle, Christina J.; Dörr, Marcus; Becke, Adrian; Nohturfft, Vivien; Weber, Stefan; Bulacher, Fabienne; Salomon, Lorena; Noah, Thierry; Birkemeyer, Ralf; Eckstein, Jens

doi:10.1093/europace/euy176

Cited by 129 publications

(100 citation statements)

References 20 publications

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“…However, in order to be accepted by the medical community, the potential value relevant to the use of these technologies needs to be proved. While the use of smartphone cameras using photoplethysmography for early detection of atrial fibrillation, based on the analysis of beat-by-beat duration variability series, has initially proved its value in a prospective two-center, international clinical validation study [40], the validation of using smartphone accelerometers is still limited and again focusing on beat-by-beat duration variability only [16], [17], [41], [42].…”

Section: Discussionmentioning

confidence: 99%

Assessment of Ultra-Short Heart Variability Indices Derived by Smartphone Accelerometers for Stress Detection

Landreani¹,

Faini²,

Martín-Yebra³

et al. 2019

Preprint

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Body acceleration due the heartbeat-induced reaction forces can be measured as smartphone accelerometer (m-ACC) signals. Our aim was to test the feasibility of using m-ACC to detect changes induced by stress by ultra-short heart rate variability (USV) indices (SDNN and RMSSD). Sixteen healthy volunteers were recruited; m-ACC was recorded while in supine position, during spontaneous breathing (REST) and during one minute of mental stress (MS) induced by arithmetic serial subtraction task, simultaneous with conventional ECG. Beat occurrences were extracted from both ECG and m-ACC and used to compute USV indices using 60, 30 and 10s durations, both for REST and MS. A feasibility of 93.8% in the beat-to-beat m-ACC heart rate series extraction was reached. In both ECG and m-ACC series, compared to REST, in MS the mean beat duration was reduced by 15% and RMSSD decreased by 38%. These results show that short term recordings (up to 10 sec) of cardiac activity using smartphone’s accelerometers are able to capture the decrease in parasympathetic tone, in agreement with the induced stimulus.

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Section: Discussionmentioning

confidence: 99%

Assessment of Ultra-Short Heart Variability Indices Derived by Smartphone Accelerometers for Stress Detection

Landreani¹,

Faini²,

Martín-Yebra³

et al. 2019

Preprint

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“…We included 10 primary diagnostic accuracy studies 15,16,[27][28][29][30][31][32][33][34] 27 which was excluded from the primary analysis, as described in the Methods section. The applications analyzed the pulsewave signal for a mean (range) of 2 (1)(2)(3)(4)(5) minutes.…”

Section: Study Selection and Characteristicsmentioning

confidence: 99%

“…We conducted a number of sensitivity analyses that explored the associations of methodological variation with our results, including investigating the associations of verification bias, which occurred in 2 studies. 30,31 Two studies 15,34 excluded results based on PPG signal quality (7.3% for 1 study 15 Preventicus with 95% CI Cardiio Rhythm with 95% CI FibriCheck with 95% CI PULSE-SMART with 95% CI…”

Section: Quality Assessmentmentioning

confidence: 99%

“…14 Given that more than 265 million people own smartphones in the United States (>80% of the population), smartphone camera applications have been proposed as accessible and widespread screening tools. 15,16 These applications, which do not require any accessories, measure a user's fingertip pulse via the camera and analyze the timing and morphology of a photoplethysmography (PPG) signal obtained via the pulse. Several studies have assessed the accuracy of smartphone camera applications for diagnosing AF, but all have been small, single studies.…”

Section: Introductionmentioning

confidence: 99%

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Accuracy of Smartphone Camera Applications for Detecting Atrial Fibrillation

et al. 2020

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IMPORTANCE Atrial fibrillation (AF) affects more than 6 million people in the United States; however, much AF remains undiagnosed. Given that more than 265 million people in the United States own smartphones (>80% of the population), smartphone applications have been proposed for detecting AF, but the accuracy of these applications remains unclear. OBJECTIVE To determine the accuracy of smartphone camera applications that diagnose AF. DATA SOURCES AND STUDY SELECTION MEDLINE and Embase were searched until January 2019 for studies that assessed the accuracy of any smartphone applications that use the smartphone's camera to measure the amplitude and frequency of the user's fingertip pulse to diagnose AF. DATA EXTRACTION AND SYNTHESIS Bivariate random-effects meta-analyses were constructed to synthesize data. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) of Diagnostic Test Accuracy Studies reporting guideline. MAIN OUTCOMES AND MEASURES Sensitivity and specificity were measured with bivariate random-effects meta-analysis. To simulate the use of these applications as a screening tool, the positive predictive value (PPV) and negative predictive value (NPV) for different population groups (ie, age Ն65 years and age Ն65 years with hypertension) were modeled. Lastly, the association of methodological limitations with outcomes were analyzed with sensitivity analyses and metaregressions. RESULTS A total of 10 primary diagnostic accuracy studies, with 3852 participants and 4 applications, were included. The oldest studies were published in 2016 (2 studies [20.0%]), while most studies (4 [40.0%]) were published in 2018. The applications analyzed the pulsewave signal for a mean (range) of 2 (1-5) minutes. The meta-analyzed sensitivity and specificity for all applications combined were 94.2% (95% CI, 92.2%-95.7%) and 95.8% (95% CI, 92.4%-97.7%), respectively. The PPV for smartphone camera applications detecting AF in an asymptomatic population aged 65 years and older was between 19.3% (95% CI, 19.2%-19.4%) and 37.5% (95% CI, 37.4%-37.6%), and the NPV was between 99.8% (95% CI, 99.83%-99.84%) and 99.9% (95% CI, 99.94%-99.95%). The PPV and NPV increased for individuals aged 65 years and older with hypertension (PPV, 20.5% [95% CI, 20.4%-20.6%] to 39.2% [95% CI, 39.1%-39.3%]; NPV, 99.8% [95% CI, 99.8%-99.8%] to 99.9% [95% CI, 99.9%-99.9%]). There were methodological limitations in a number of studies that did not appear to be associated with diagnostic performance, but this could not be definitively excluded given the sparsity of the data. CONCLUSIONS AND RELEVANCE In this study, all smartphone camera applications had relatively high sensitivity and specificity. The modeled NPV was high for all analyses, but the PPV was modest, (continued) Key Points Question What is the overall accuracy of smartphone camera applications that diagnose and screen for atrial fibrillation (AF)? Findings In this meta-analysis of 10 primary diagnostic accuracy studies with 3852 participants, all app...

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“…The direct to consumer or wearable technology market, comprised of devices that monitor physiological parameters such as heart rate and sleep pattern, is anticipated to grow to 929 million connected devices by 2021 87 . These devices encompass wristbands, glasses, in‐ear monitors, chest straps, and smart phone‐enabled recording electrode systems or electronic shirts, with varying capacity to monitor heart rate, heart rhythm, blood pressure, physical activity, respiratory rate, blood glucose, and sleep patterns 88–90 . For heart rate monitoring, most wearable devices use photoplethysmography (PPG) technology, meaning they are inherently less accurate than conventional electrocardiography monitoring techniques.…”

Section: General Tools For Risk Assessment Strengths Limitations Amentioning

confidence: 99%

European Heart Rhythm Association (EHRA)/Heart Rhythm Society (HRS)/Asia Pacific Heart Rhythm Society (APHRS)/Latin American Heart Rhythm Society (LAHRS) expert consensus on risk assessment in cardiac arrhythmias: use the right tool for the right outcome, in the right population

Nielsen

Lin

Figueiredo

et al. 2020

Journal of Arrhythmia

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

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

Cited by 129 publications

References 20 publications

Assessment of Ultra-Short Heart Variability Indices Derived by Smartphone Accelerometers for Stress Detection

Assessment of Ultra-Short Heart Variability Indices Derived by Smartphone Accelerometers for Stress Detection

Accuracy of Smartphone Camera Applications for Detecting Atrial Fibrillation

European Heart Rhythm Association (EHRA)/Heart Rhythm Society (HRS)/Asia Pacific Heart Rhythm Society (APHRS)/Latin American Heart Rhythm Society (LAHRS) expert consensus on risk assessment in cardiac arrhythmias: use the right tool for the right outcome, in the right population

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