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.

The inter-beat interval features derived from PPG are equivalent to the ones from ECG for AF detection. Movement artefacts substantially worsen PPG-based AF monitoring in free-living conditions, therefore monitoring coverage needs to be carefully selected. Wrist-worn PPG still provides a promising technology for long-term AF monitoring.

In this paper, we propose an algorithm that classifies whether a generated cardiac arrhythmia alarm is true or false. The large number of false alarms in intensive care is a severe issue. The noise peaks caused by alarms can be high and in a noisy environment nurses can experience stress and fatigue. In addition, patient safety is compromised because reaction time of the caregivers to true alarms is reduced. The data for the algorithm development consisted of records of electrocardiogram (ECG), arterial blood pressure, and photoplethysmogram signals in which an alarm for either asystole, extreme bradycardia, extreme tachycardia, ventricular fibrillation or flutter, or ventricular tachycardia occurs. First, heart beats are extracted from every signal. Next, the algorithm selects the most reliable signal pair from the available signals by comparing how well the detected beats match between different signals based on [Formula: see text]-score and selecting the best match. From the selected signal pair, arrhythmia specific features, such as heart rate features and signal purity index are computed for the alarm classification. The classification is performed with five separate Random Forest models. In addition, information on the local noise level of the selected ECG lead is added to the classification. The algorithm was trained and evaluated with the PhysioNet/Computing in Cardiology Challenge 2015 data set. In the test set the overall true positive rates were 93 and 95% and true negative rates 80 and 83%, respectively for events with no information and events with information after the alarm. The overall challenge scores were 77.39 and 81.58.