A novel application for the detection of an irregular pulse using an iPhone 4S in patients with atrial fibrillation

McManus, David D.; Lee, Jin-Seok; Maitas, Oscar; Esa, Nada; Pidikiti, Rahul; Carlucci, Alex; Harrington, Josephine; Mick, Eric; Chon, Ki H.

doi:10.1016/j.hrthm.2012.12.001

Cited by 236 publications

(212 citation statements)

References 21 publications

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“…Such different techniques could be categorized into solutions aimed at either instantaneous or continuous patient assessment. Applications using data provided by the camera sensor and lamp of a smartphone have been extensively validated to record pulse rate variability at the fingertip and identify AF during a spot check 29, 30. Continuous PPG measurements have been also investigated in very recent studies using earlobe‐, finger‐, and wrist‐wearable sensors 31, 32, 33.…”

Section: Discussionmentioning

confidence: 99%

Atrial Fibrillation Detection Using a Novel Cardiac Ambulatory Monitor Based on Photo‐Plethysmography at the Wrist

Bonomi

Schipper

Eerikäinen

et al. 2018

JAHA

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BackgroundLong‐term continuous cardiac monitoring would aid in the early diagnosis and management of atrial fibrillation (AF). This study examined the accuracy of a novel approach for AF detection using photo‐plethysmography signals measured from a wrist‐based wearable device.Methods and Results ECG and contemporaneous pulse data were collected from 2 cohorts of AF patients: AF patients (n=20) undergoing electrical cardioversion (ECV) and AF patients (n=40) that were prescribed for 24 hours ECG Holter in outpatient settings (HOL). Photo‐plethysmography and acceleration data were collected at the wrist and processed to determine the inter‐pulse interval and discard inter‐pulse intervals in presence of motion artifacts. A Markov model was deployed to assess the probability of AF given irregular pattern in inter‐pulse interval sequences. The AF detection algorithm was evaluated against clinical rhythm annotations of AF based on ECG interpretation. Photo‐plethysmography recordings from apparently healthy volunteers (n=120) were used to establish the false positive AF detection rate of the algorithm. A total of 42 and 855 hours (AF: 21 and 323 hours) of photo‐plethysmography data were recorded in the ECV and HOL cohorts, respectively. AF was detected with >96% accuracy (ECV, sensitivity=97%; HOL, sensitivity=93%; both with specificity=100%). Because of motion artifacts, the algorithm did not provide AF classification for 44±16% of the monitoring period in the HOL group. In healthy controls, the algorithm demonstrated a <0.2% false positive AF detection rate.ConclusionsA novel AF detection algorithm using pulse data from a wrist‐wearable device can accurately discriminate rhythm irregularities caused by AF from normal rhythm.

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Section: Discussionmentioning

confidence: 99%

Atrial Fibrillation Detection Using a Novel Cardiac Ambulatory Monitor Based on Photo‐Plethysmography at the Wrist

Bonomi

Schipper

Eerikäinen

et al. 2018

JAHA

Self Cite

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“…A recent systematic review and meta‐analysis found that modified sphygmomanometers have a pooled sensitivity of 98% and specificity of 92%, whereas non–12‐lead ECGs have a sensitivity of 91% and a specificity of 95% in detecting an irregular pulse and suspected AF 27. Recently, several studies have reported the use of smartphone cameras to differentiate between AF and sinus rhythm using fingertip photoplethysmographic signals 16, 28, 29. In a study by McManus et al, fingertip photoplethysmographic signal recorded using an iPhone 4S in 76 patients before and after cardioversion had a sensitivity of 96% and specificity of 97% in discriminating AF from sinus rhythm 28.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, several studies have reported the use of smartphone cameras to differentiate between AF and sinus rhythm using fingertip photoplethysmographic signals 16, 28, 29. In a study by McManus et al, fingertip photoplethysmographic signal recorded using an iPhone 4S in 76 patients before and after cardioversion had a sensitivity of 96% and specificity of 97% in discriminating AF from sinus rhythm 28. In another study using the same algorithm and iPhone 4S in 80 inpatients and outpatients yielded a reduced sensitivity and specificity of 90% and 85% 29.…”

Section: Discussionmentioning

confidence: 99%

Contact‐Free Screening of Atrial Fibrillation by a Smartphone Using Facial Pulsatile Photoplethysmographic Signals

Yan

Lai

Chan

et al. 2018

JAHA

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BackgroundWe aimed to evaluate a novel method of atrial fibrillation (AF) screening using an iPhone camera to detect and analyze photoplethysmographic signals from the face without physical contact by extracting subtle beat‐to‐beat variations of skin color that reflect the cardiac pulsatile signal.Methods and ResultsPatients admitted to the cardiology ward of the hospital for clinical reasons were recruited. Simultaneous facial and fingertip photoplethysmographic measurements were obtained from 217 hospital inpatients (mean age, 70.3±13.9 years; 71.4% men) facing the front camera and with an index finger covering the back camera of 2 independent iPhones before a 12‐lead ECG was recorded. Backdrop and background light intensity was monitored during signal acquisition. Three successive 20‐second (total, 60 seconds) recordings were acquired per patient and analyzed for heart rate regularity by Cardiio Rhythm (Cardiio Inc, Cambridge, MA) smartphone application. Pulse irregularity in ≥1 photoplethysmographic readings or 3 uninterpretable photoplethysmographic readings were considered a positive AF screening result. AF was present on 12‐lead ECG in 34.6% (n=75/217) patients. The Cardiio Rhythm facial photoplethysmographic application demonstrated high sensitivity (95%; 95% confidence interval, 87%–98%) and specificity (96%; 95% confidence interval, 91%–98%) in discriminating AF from sinus rhythm compared with 12‐lead ECG. The positive and negative predictive values were 92% (95% confidence interval, 84%–96%) and 97% (95% confidence interval, 93%–99%), respectively.ConclusionsDetection of a facial photoplethysmographic signal to determine pulse irregularity attributable to AF is feasible. The Cardiio Rhythm smartphone application showed high sensitivity and specificity, with low negative likelihood ratio for AF from facial photoplethysmographic signals. The convenience of a contact‐free approach is attractive for community screening and has the potential to be useful for distant AF screening.

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“…Second, there was no significant difference in the results from different smart devices compared with ECG, and the model of smartphone had little impact on the diagnostic performance of the algorithm Prior to the development of the PRO AF PPG algorithm, several algorithms were validated for the detection of AF based on smartphones and wearable devices (Table 3). Root mean square of successive difference of RR intervals (RMSSD), Shannon entropy (ShE), Poincaré plot analysis (PPA), and the AliveCor automated algorithm have been used to discriminate between AF and SR by analyzing pulse waveform signals recorded using smart devices in several recent studies [19,21,24,25].…”

Section: Principal Findingsmentioning

confidence: 99%

“…McManus et al [24] described on an application using a camera and LED light of an iPhone 4S to record pulse waves obtained from the fingertips of patients. The signal recorded was processed through an algorithm combining RMSSD and ShE.…”

Section: Principal Findingsmentioning

confidence: 99%

Diagnostic Performance of a Smart Device With Photoplethysmography Technology for Atrial Fibrillation Detection: Pilot Study (Pre-mAFA II Registry) (Preprint)

Fan¹,

Li²,

Li³

et al. 2018

Preprint

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Background: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia. The asymptomatic nature and paroxysmal frequency of AF lead to suboptimal early detection. A novel technology, photoplethysmography (PPG), has been developed for AF screening. However, there has been limited validation of smartphone and smart band applications with PPG compared to 12-lead electrocardiograms (ECG). Objective:We investigated the feasibility and accuracy of a smartphone-and smart band-based algorithm (PRO AF PPG) for AF detection using pulse data measured by PPG. Methods:One hundred twelve consecutive inpatients were recruited from the Chinese PLA General Hospital from 15 March to 1 April 2018. Participants were simultaneously tested with smartphones (HUAWEI Mate 9, HUAWEI Honor 7X), smart bands (HUAWEI Band 2), and 12-lead ECG for 3 minutes. Results:One hundred eight patients (56 with normal sinus rhythm, 52 with persistent AF) were enrolled in the final analysis after excluding 4 patients with unclear cardiac rhythms. The corresponding sensitivity and specificity of the smart band PPG were 95.36% (95% confidential interval, CI 92.00-97.40%) and 99.70% (95% CI 98.08-99.98%), respectively. The positive predictive value of the smart band PPG was 99.63% (95% CI 97.61-99.98%), the negative predictive value was 96.24% (95% CI 93.50-97.90%), and the accuracy was 97.72% (95% CI 96.11-98.70%). Moreover, the diagnostic sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of smartphones with PPG for AF detection were over 94%. There was no significant difference after further statistical analysis of the results from the different smart devices compared with the gold-standard ECG (P=.999). Conclusions:The algorithm based on smartphones and smart bands with PPG demonstrated good performance in detecting AF and may represent a convenient tool for AF detection in at-risk individuals, allowing widespread screening of AF in the population.

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A novel application for the detection of an irregular pulse using an iPhone 4S in patients with atrial fibrillation

Cited by 236 publications

References 21 publications

Atrial Fibrillation Detection Using a Novel Cardiac Ambulatory Monitor Based on Photo‐Plethysmography at the Wrist

Atrial Fibrillation Detection Using a Novel Cardiac Ambulatory Monitor Based on Photo‐Plethysmography at the Wrist

Contact‐Free Screening of Atrial Fibrillation by a Smartphone Using Facial Pulsatile Photoplethysmographic Signals

Diagnostic Performance of a Smart Device With Photoplethysmography Technology for Atrial Fibrillation Detection: Pilot Study (Pre-mAFA II Registry) (Preprint)

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