AHA Journal – Optimizing a Photophletysmography Algorhithm Using Crowdsourcing Research Data

AHA Journal – Optimizing a Photophletysmography Algorhithm Using Crowdsourcing Research Data

15 May, 2020

Introduction: Photoplethysmography (PPG) records the reflection of light in blood perfused tissues, and can be used for heart rhythm analysis. Advances in smartphone technology allow real time PPG recordings and analysis for heart rate. Atrial fibrillation (AF) is the most common arrhythmia. Early detection of AF can prevent strokes and their devastating sequels. Due to its irregularity proper adjudication of AF with PPG is dependent on signal quality and signal-to-noise ratio. We initiated a crowd sourced data donation campaign to collect PPG signals and train our algorithm in signal quality assessment and noise detection.

Hypothesis: Crowdsourcing is an effective method to collect a large number of PPG measurements to train an AF detection algorithm.

Methods: In 2017 the Heart for Heart campaign was initiated through a collaboration of cardiologists, Happitech, Arrhythmia alliance and Sudler-team. In an online campaign participants were asked to complete a 90 seconds PPG measurement and a questionnaire with demographic and arrhythmia history.

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