Detection of Atrial Fibrillation Using PPG From Smartphone Cameras

Detection of Atrial Fibrillation Using Photoplethysmography From Smartphone Cameras

The protocol is designed to enroll patients from at least 3 diverse clinical sites with smartphones who are being evaluated for the presence or absence of atrial fibrillation (AF) using the Heart Rhythm Software algorithm running on their smartphones. Algorithm output will be compared to a gold standard of 12-lead ECG recorded with a simultaneous PPG measurement.

Atrial fibrillation (AF) is the most common sustained arrhythmia worldwide, and its prevalence is increasing and is expected to continue increasing for decades,. Detection of AF episodes may be useful for the identification of undiagnosed AF and in making clinical management decisions that include oral anticoagulation, restoration and maintenance of sinus rhythm, control of ventricular rate, and risk factor modification. Treatment of AF is associated with improvements of quality of life, stroke prevention, and reduced risk of heart failure.

Traditionally, diagnosis of AF is done using an electrocardiogram, with low amplitude or absent p-waves and narrow complex "irregularly irregular" pattern intervals seen on the ECG,. After diagnosis, there are a variety of methods that can be used to manage AF. Ambulatory ECG monitoring, implantable monitoring devices, and over-the-counter wrist-worn devices have been used to manage patients with AF,,. However, these devices are inconvenient and expensive which limits their wide adoption into clinical practice. There is an unmet clinical need for an easy to use and inexpensive over-the-counter FDA-cleared products that can detect episodes of AF in real-time and notify patients to seek out further care when AF is suspected.

Recent technological advancements have allowed for the identification of AF using smartwatches, with device manufacturers gaining over-the-counter regulatory clearance for their irregular heart rate detection algorithms,. These wrist-worn devices use a light-based measurement method called photoplethysmography (PPG) to measure changes in light absorbance and reflectance to understand changes in blood flow in superficial vasculature. The waveform generated from this measurement can be used to measure parameters like pulse rate, respiratory rate, pulse rate variability and others in addition to detecting AF. The increasing ubiquity of smartphones worldwide has led to greater availability of heart rhythm diagnostics using smartphone photoplethysmography (PPG). Smartphone camera PPG technology uses the light-emitting diode in cameras to measure pulsatile changes in light intensity that are reflected from a finger and can be used to detect AF. The widespread availability of this technology has the potential to improve the accessibility and convenience of heart rhythm monitoring, particularly in remote or underserved areas. This could result in improvements in early detection and management of AF, leading to better patient outcomes. Several smartphone PPG-based algorithms have been developed that utilize smartphones' built-in cameras to detect AF. The sensitivity and specificity of these algorithms for the detection of AF range between 81%-100% and 85%-100% respectively. However, there is a lack of evidence regarding the performance of these algorithms in detecting AF in an out-of-hospital setting using a gold-standard reference.

Happitech has developed a proprietary software development kit (SDK) that can be utilized to measure patients' cardiovascular parameters and identify potential pathologies including AF. Happitech's algorithm requires users to place a digit directly against their phone camera, and the camera and underlying algorithm detect subtle differences in blood flow to calculate physiological parameters for monitoring and clinical use.

The purpose of this study is to validate the performance of Happitech's PPG-based algorithm for the detection of AF against a 12-lead ECG in a diverse population of patients including cardiology patients being evaluated as inpatients or at a cardiology clinic visit.

Inclusion Criteria:

1. Adults aged 22 years or older
2. Owns a smartphone and knows how to operate and navigate different applications.
3. Individuals able to read, understand, and sign the consent form.
4. Cardiology patients consulting the cardiac outpatient clinic.
5. History of persistent AF or other cardiac rhythm

Exclusion Criteria:

1. Participants with cognitive impairments
2. Individuals who cannot read, speak, and/or understand English
3. Patients with implantable neurostimulators, cardiac implantable device in an atrial or ventricular paced rhythm documented on a 12-lead ECG during the initial study visit