Atrial fibrillation detection using photo-plethysmography and acceleration data at the wrist

Atrial fibrillation (AF) is a pathological cardiac condition leading to increased risk for embolic stroke. Screening for AF is challenging due to the paroxysmal and asymptomatic nature of the condition. We aimed to investigate whether an unobtrusive wrist-wearable device equipped with a photo-plethysmographic (PPG) and acceleration sensor could detect AF. Sixteen patients with suspected AF were monitored for 24 hours in an outpatient setting using a Holter ECG. Simultaneously, PPG and acceleration data were collected at the wrist. PPG data was processed to determine the timing of heartbeats and inter-beat-interval (IBI). Wrist acceleration and PPG morphology were used to discard IBIs in presence of motion artefacts. An ECG validated first-order Markov model was used to assess the probability of irregular rhythm due to AF using PPG-derived IBIs. The AF detection algorithm was compared with clinical adjudications of AF episodes after review of the ECG records. AF detection was achieved with 97 ± 2% sensitivity and 99 ± 3% specificity. Due to motion artefacts, the algorithm did not provide AF classification for an average of 36 ± 9% of the 24 hours monitoring. We concluded that a wrist-wearable device equipped with a PPG and acceleration sensor can accurately detect rhythm irregularities caused by AF in daily life