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.
Objective: Photoplethysmography (PPG) enables unobtrusive heart rate monitoring, which can be used in wrist-worn applications. Its potential for detecting atrial fibrillation (AF) has been recently presented. Besides AF, another cardiac arrhythmia increasing stroke risk and requiring treatment is atrial flutter (AFL). Currently, the knowledge about AFL detection with PPG is limited. The objective of our study was to develop a model that classifies AF, AFL, and sinus rhythm with or without premature beats from PPG and acceleration data measured at the wrist in daily life. Methods: A dataset of 40 patients was collected by measuring PPG and accelerometer data, as well as electrocardiogram as a reference, during 24-hour monitoring. The dataset was split into 75%-25% for training and testing a Random Forest (RF) model, which combines features from PPG, inter-pulse intervals (IPI), and accelerometer data, to classify AF, AFL, and other rhythms. The performance was compared to an AF detection algorithm combining traditional IPI features for determining the robustness of the accuracy in presence of AFL. Results: The RF model classified AF/AFL/other with sensitivity and specificity of 97.6/84.5/98.1% and 98.2/99.7/92.8%, respectively. The results with the IPI-based AF classifier showed that the majority of false detections were caused by AFL. Conclusion: The PPG signal contains information to classify AFL in the presence of AF, sinus rhythm, or sinus rhythm with premature contractions. Significance: PPG could indicate presence of AFL, not only AF.
Detecting episodes of bradycardia and tachycardia can help identifying the clinical relevance of common cardiac symptoms. This study aimed at investigating whether an unobtrusive wrist-wearable device equipped with a photoplethysmographic (PPG) and acceleration sensor could be used to detect such rate abnormalities in free-living conditions. Twenty patients (M=55%, age: 67 ± 13 y) reporting cardiac symptoms were monitored for 24 hours in free-living conditions using a portable Holter ECG recorder. Simultaneously, a wrist-wearable device equipped with a PPG and acceleration sensor was used to measure heart rate and the mean inter-pulse-interval (IPI) in 5-sec epochs. ECG-derived inter-beat-intervals (IBI) were used as ground truth for determining episodes of bradycardia (>1200 ms) and tachycardia (<500 ms) during the monitoring period. According to the ECG, the duration of brady-and tachycardia and normal rate lasted a total of 766 min, 64 min, and 27024 min, respectively. Average IPI during bradycardia and tachycardia was 1310 ± 80 ms and 459 ± 37 ms, respectively. IPI data correctly identified episodes of bradycardia (Se: 85.0%, Sp: 99.4%) and tachycardia (Se: 89.1%, Sp: 99.9%). In conclusion, a wrist-wearable device equipped with a PPG sensor can accurately detect rate abnormalities such as brady-and tachycardia in free-living conditions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.