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Related Experiment Video

Updated: Jan 5, 2026

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

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Published on: December 11, 2019

Atrial Fibrillation Detection from Wrist Photoplethysmography Signals Using Smartwatches.

Syed Khairul Bashar¹, Dong Han¹, Shirin Hajeb-Mohammadalipour¹

¹Department of Biomedical Engineering, University of Connecticut, Storrs, CT, USA.

Scientific Reports

|October 23, 2019

Summary

This study presents a novel method for detecting atrial fibrillation (AF) using smart wrist watch photoplethysmogram (PPG) signals. The approach effectively identifies AF and filters out motion artifacts, achieving high accuracy.

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Area of Science:

Biomedical Engineering
Cardiology
Signal Processing

Background:

Atrial fibrillation (AF) detection is crucial for continuous arrhythmia monitoring.
Wrist-worn devices offer a non-invasive, convenient platform for long-term health tracking.
Photoplethysmogram (PPG) signals from wearables are promising for AF detection but susceptible to noise.

Purpose of the Study:

To develop a novel method for detecting AF from smart wrist watch PPG signals.
To accurately identify and mitigate motion and noise artifacts in PPG recordings.
To improve the reliability and accuracy of AF detection in wearable devices.

Main Methods:

Utilized accelerometer data and time-frequency analysis (variable frequency complex demodulation) to detect motion and noise artifacts.
Calculated root mean square of successive differences and sample entropy from beat-to-beat intervals for AF discrimination.
Incorporated a premature atrial contraction detection algorithm to enhance AF identification and reduce false positives.

Main Results:

The proposed method demonstrated high efficacy in detecting AF from wrist-worn PPG signals.
Achieved a combined sensitivity of 98.18%, specificity of 97.43%, and accuracy of 97.54% across two independent datasets.
Successfully distinguished between normal sinus rhythm and AF while accounting for signal corruption.

Conclusions:

The developed method offers a robust solution for AF detection using smart wrist watch PPG signals.
The integration of artifact detection significantly improves the reliability of wearable-based arrhythmia monitoring.
This technology holds potential for widespread, non-invasive, long-term AF screening and management.