A new smart wristband equipped with an artificial intelligence algorithm to detect atrial fibrillation

Abstract

Background: Detection of atrial fibrillation (AF) occurrence over a long duration has been a challenge in the screening and follow-up of AF patients. Wearable devices may be an ideal solution.

Objective: The purpose of this study was to measure the sensitivity, specificity, and accuracy of a recently developed smart wristband device that is equipped with both photoplethysmographic (PPG) and single-channel electrocardiogram (ECG) systems and an AF-identifying, artificial intelligence (AI) algorithm, used in the short term.

Methods: Use of the Amazfit Health Band 1S, which records both PPG and single-channel ECG data, was assessed in 401 patients (251 normal individuals and 150 ECG-diagnosed AF patients).

Results: ECG and PPG readings could not be judged in 15 and 18 subjects, respectively. Subjects who were unable to be judged were defined as either false negative or false positive. The sensitivity, specificity, and accuracy of wristband PPG readings were 88.00%, 96.41%, and 93.27%, respectively, and those of wristband ECG readings were 87.33%, 99.20%, and 94.76%, respectively. When the original wristband ECG records were judged by physicians, the sensitivity, specificity, and accuracy were 96.67%, 98.01%, and 97.51%, respectively.

Conclusion: This promising new combination of PPG, ECG, and AI algorithm has the potential to facilitate AF detection.

Keywords: Artificial intelligence; Atrial fibrillation; Electrocardiogram; Photoplethysmography; Wearable electronic device.