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Atrial fibrillation (AF) is a common cardiac arrhythmia with serious healthconsequences if not detected and treated early. Detecting AF using wearabledevices with photoplethysmography (PPG) sensors and deep neural networks hasdemonstrated some success using proprietary algorithms in commercial solutions.However, further advancement of this paradigm of continuous AF detection inambulatory settings, towards a population-wide screening use case, still facesseveral challenges, one of which is the lack of large-scale labeled trainingdata. To address this challenge, in this study, we propose to leverage AFalarms from bedside patient monitors to label concurrent PPG signals, resultingin the largest PPG-AF dataset so far (8.5M 30-second records from 24100patients) and demonstrating a practical approach to build large labeled PPGdatasets. Furthermore, we recognize that the AF labels thus obtained containerrors because of false AF alarms generated from imperfect built-in algorithmsfrom bedside monitors. Dealing with label noise with unknown distributioncharacteristics in this case requires advanced algorithms. We, therefore,introduce and open source a novel loss design, the cluster membershipconsistency (CMC) loss, to mitigate label errors. By comparing CMC withstate-of-the-art methods selected from a noisy label competition, wedemonstrate its superiority in multiple aspects including handling label noisein PPG data, resilience to poor-quality signals, and computational efficiency.

Learning From Alarms: A Robust Learning Approach for Accurate  Photoplethysmography-Based Atrial Fibrillation Detection using Eight Million  Samples Labeled with Imprecise Arrhythmia Alarms