Key result
A Random Forest model using wrist-measured photoplethysmography classified atrial fibrillation, atrial flutter, and other rhythms with sensitivities of 97.6%, 84.5%, and 98.1%, respectively.
Why the study?
Photoplethysmography (PPG) has potential for detecting AF, but knowledge about PPG detection of atrial flutter, another arrhythmia that increases stroke risk, is limited.
Does a Random Forest model using wrist-worn PPG and accelerometer data accurately classify atrial fibrillation and atrial flutter in patients undergoing 24-hour monitoring?
Observational (n=39)
No
Does a Random Forest model using wrist-worn PPG and accelerometer data accurately classify atrial fibrillation and atrial flutter in patients undergoing 24-hour monitoring?
Answers are grounded in this paper and cite their sources.
Wrist-worn PPG and accelerometer data analyzed with a Random Forest model can accurately detect both atrial fibrillation and atrial flutter in daily life.
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.
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Eerikäinen et al. (2019) conducted an observational in Atrial Fibrillation and Atrial Flutter (n=39). Random Forest model using photoplethysmography and accelerometer data vs. ECG reference (ground truth) and IPI-based AF classifier was evaluated on Sensitivity and specificity for classifying AF, AFL, and other rhythms. A Random Forest model using wrist-measured photoplethysmography classified atrial fibrillation, atrial flutter, and other rhythms with sensitivities of 97.6%, 84.5%, and 98.1%, respectively.
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