A novel application for the detection of an irregular pulse using an iPhone 4S in patients with atrial fibrillation
Introduction
Atrial fibrillation (AF) is the most commonly diagnosed dysrhythmia, affecting approximately 3 million Americans.1, 2 AF negatively affects quality of life and survival, placing those with dysrhythmia at an increased risk for stroke and heart failure.3, 4 Although the 12-lead electrocardiogram (ECG) remains the gold-standard diagnostic test for AF,5 a major challenge in the diagnosis of this arrhythmia is its paroxysmal nature, particularly in its early stages.6 Recent studies have shown that more frequent monitoring can improve AF detection,7 but contemporary monitoring technologies used for AF detection in clinical practice are costly and sometimes burdensome. Given these difficulties, a recent National Health, Heart, Lung, and Blood Institute expert panel has emphasized the pressing need to develop new methods for accurate AF detection and monitoring.8
On the basis of previously published data from our laboratory and elsewhere,9, 10, 11 we hypothesized that an irregular pulse could be identified using recordings from an iPhone 4S camera combined with an accurate and real-time realizable AF detection algorithm.12 In this original investigation, we report the beat-to-beat and overall detection capabilities of our novel algorithm running on an iPhone 4S in a prospectively recruited cohort of patients with persistentAF.
Section snippets
Study sample
Seventy-six adults with AF were identified from a roster of patients scheduled to undergo elective cardioversion for AF at the University of Massachusetts Medical Center’s Cardiac Electrophysiology Laboratory. After obtaining informed consent, baseline clinical, demographic, laboratory, and electrophysiologic variables, as well as postprocedure heart rate and blood pressure, were abstracted from participants’ medical records by trained study staff. Subjects with AF on their preprocedure 12-lead
Results
The baseline characteristics of the 76 participants with AF included in our prospective clinical investigation are shown in Table 1. The mean age of the cohort was 65 years of age, and 35% were women. There was a high burden of cardiovascular morbidity at study entry in the cohort.
Participants in AF had significantly higher heart rates, respiratory rates, and systolic and diastolic blood pressures before cardioversion than they did after their successful cardioversion (Table 2). RMSSD/mean and
Discussion
Several prior investigations have described the use of a smartphone to detect an irregular pulse during AF.9, 11, 14, 16, 17 In contradistinction to previously described systems, our application does not require additional hardware, instead rely on the iPhone 4S camera and lamp to obtain pulse recordings, and is not bedeviled by motion and noise artifacts.15, 16, 17, 18, 19 Two prior investigations from our group introduced the concept of using a camera to extract RR intervals and established
Conclusions
In our moderately sized prospective cohort study involving 76 patients with AF undergoing cardioversion, we observed that 2 statistical methods (RMSDD/mean and ShE) were strongly related to AF and that a novel arrhythmia detection application combining these 2 statistical methods reliably distinguished an irregular pulse from AF from pulse waveforms obtained during NSR. Since our application is accurate and real-time realizable using hardware that already exists within a standard smartphone, we
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2021, Cardiac Electrophysiology ClinicsCitation Excerpt :Although details of many signal processing methods are proprietary and therefore not published, the general computational approach to distinguishing sinus rhythm from AF exploits AF’s irregular and random ventricular activation. For a given period of time, the PPG waveform is analyzed for variation in beat-to-beat intervals and morphology as well as the pattern’s overall complexity and unpredictability.17 Additionally, information extracted from the device’s built-in accelerometer allows for filtering noise and motion artifact, which are major sources of error.21
This work was funded in part by the Office of Naval Research work unit N00014-12-1-0171. Dr McManus’s time was funded by National Institutes of Health through grants 1U01HL105268-01 and KL2RR031981.
Dr McManus, Dr Lee, and Dr Chon have ownership stake in DxMe, Inc. Dr Chon has a patent on the algorithm described in the article.
The first 2 authors contributed equally to this article.