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

Abstract

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.

Current progress of photoplethysmography and SPO2 for health monitoring

“…With and without the Markov model, the specificities were 95, 89, 92, and 88%, respectively. As a result, the AI algorithm improved the specifications, comparable with the Food and Drug Administration-approved ECG-based Alive-Cor automated AF detector [159].…”

“…Measurement of the HR during rest and exercise by most wrist monitors is highly accurate [144,157]. HR monitoring would be valuable to ensure that AF patients are adequately rate-controlled, to mitigate symptoms and decompensation [158,159]. These technologies show promise for long-term noninvasive monitoring of AF.…”

Premature Atrial and Ventricular Contraction Detection Using Photoplethysmographic Data from a Smartwatch

“…Indeed, we have shown that detecting PAC/PVC can lead to significant false positive reduction during AF detection [ 40 ]. Other recent published reports [ 14 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ] have largely focused on AF without accounting for PAC/PVC and consequently their accuracy of AF detection was suboptimal.…”

Machine learning detection of Atrial Fibrillation using wearable technology

“…[3] There has been recent interest in the use of consumer devices for the detection of arrhythmias including AF. [5,6] Wrist-worn heart-rate sensors that measure heart rate using Light Emitting Diodes (LEDs) have become increasingly popular but their accuracy has been questioned and some manufacturers advise the heart rate measurement should be considered an estimate and suggest using a chest heart rate sensor for increased accuracy.…”