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Wearable devices enable theoretically continuous, longitudinal monitoring ofphysiological measurements like step count, energy expenditure, and heart rate.Although the classification of abnormal cardiac rhythms such as atrialfibrillation from wearable devices has great potential, commercial algorithmsremain proprietary and tend to focus on heart rate variability derived fromgreen spectrum LED sensors placed on the wrist where noise remains an unsolvedproblem. Here, we develop a multi-task deep learning method to assess signalquality and arrhythmia event detection in wearable photoplethysmography devicesfor real-time detection of atrial fibrillation (AF). We train our algorithm onover one million simulated unlabeled physiological signals and fine-tune on acurated dataset of over 500K labeled signals from over 100 individuals from 3different wearable devices. We demonstrate that in comparison with atraditional random forest-based approach (precision:0.24, recall:0.58, f1:0.34,auPRC:0.44) and a single task CNN (precision:0.59, recall:0.69, f1:0.64,auPRC:0.68) our architecture using unsupervised transfer learning throughconvolutional denoising autoencoders dramatically improves the performance ofAF detection in participants at rest (pr:0.94, rc:0.98, f1:0.96, auPRC:0.96).In addition, we validate algorithm performance on a prospectively derivedreplication cohort of ambulatory subjects using data derived from anindependently engineered device. We show that two-stage training can helpaddress the unbalanced data problem common to biomedical applications wherelarge well-annotated datasets are scarce. In conclusion, though a combinationof simulation and transfer learning and we develop and apply a multitaskarchitecture to the problem of AF detection from wearable wrist sensorsdemonstrating high levels of accuracy and a solution for the vexing challengeof mechanical noise.

DeepBeat: A multi-task deep learning approach to assess signal quality and arrhythmia detection in wearable devices