Abstract 55: Detection of Atrial Fibrillation Using a Smartphone Camera

Abstract

Introduction: Atrial fibrillation (AF) is the most common sustained dysrhythmia in the U.S. and is associated with significant morbidity and mortality. Although new AF treatment strategies have emerged over the last decade, a major challenge facing clinicians and researchers is the paroxysmal, often short-lived, and sometimes asymptomatic nature of AF. We hypothesized that a smartphone can detect AF based on its ability to record heart beat variability by placing a fingertip on the built-in camera. Methods: In this prospective clinical investigation, we recruited 33 participants at a tertiary care medical center who presented with persistent AF prior to elective cardioversion. We recorded 2-5 minutes of pulse readings from participants’ index finger using an iPhone 4S before (AF cases), and after, electrical cardioversion (Sinus rhythm controls). With heart beat interval time series obtained from changes in color signals of a fingertip recording, we compared the results of an AF detection algorithm using Root Mean Square of Successive Difference in combination with Shannon Entropy and Sample Entropy in comparison to the gold-standard of an electrocardiogram. Results: The AF detection accuracy was 97.67% (sensitivity of 96.08% and specificity of 99.97%) for each sample when a combination of the three signal processing techniques were applied compared to the gold-standard electrocardiographic recordings. All of the 33 AF episodes were detected. In addition, we tested on 29 healthy subjects and found that the accuracy (specificity) was 99.51%. Conclusions: In this paper, we show that a smartphone can accurately detect AF based on its ability to obtain RR time series. More accessible methods to accurately detect AF are necessary, and the use of existing smartphone technologies has potentially revolutionary clinical applications for in-home monitoring for AF.

Smartphone detection of atrial fibrillation using photoplethysmography: a systematic review and meta-analysis

“…Of the 28 studies included, 25 were prospective, of which 19 were conducted in a single centre9 11–29 and 6 involved two or more centres 23 30–34. Sixteen studies were conducted in secondary care,9 12–20 22 25 26 29–31 seven in primary care,11 21 23 24 32–34 three were unspecified27 28 35 and three used retrospective databases 35 36. Nine studies conducted in secondary care included those with known AF scheduled for cardioversion or catheter ablation 9 13–15 19 20 22 25 26.…”

“…Sixteen studies were conducted in secondary care,9 12–20 22 25 26 29–31 seven in primary care,11 21 23 24 32–34 three were unspecified27 28 35 and three used retrospective databases 35 36. Nine studies conducted in secondary care included those with known AF scheduled for cardioversion or catheter ablation 9 13–15 19 20 22 25 26. Details on inclusion and exclusion criteria for each study are presented in table 1 (and online supplemental table S3 for conference abstracts).…”

“…In terms of devices, 16 studies used an Apple iOS smartphone,9 11 13–16 18–20 26 29–32 36 1 study used an Android smartphone, 12 a further 2 studies used a combination22 25 and 9 studies did not specify the smartphone used 17 21 23 24 27 28 33–35. The most common PPG applications used were Cardiio Rhythm mobile application (CRMA; eight studies), Fibricheck (four studies), Preventicus (five studies) and PULSESMART (two studies), with nine studies not specifying the application 13 19 20 22 23 25 36. A 12-lead ECG was used as a reference standard in 13 studies, while 11 studies used a single-lead ECG and 4 studies used a combination of 12-lead, single or 3-lead ECG.…”