  Wearable devices enable theoretically continuous, longitudinal monitoring of
physiological measurements like step count, energy expenditure, and heart rate.
Although the classification of abnormal cardiac rhythms such as atrial
fibrillation from wearable devices has great potential, commercial algorithms
remain proprietary and tend to focus on heart rate variability derived from
green spectrum LED sensors placed on the wrist where noise remains an unsolved
problem. Here, we develop a multi-task deep learning method to assess signal
quality and arrhythmia event detection in wearable photoplethysmography devices
for real-time detection of atrial fibrillation (AF). We train our algorithm on
over one million simulated unlabeled physiological signals and fine-tune on a
curated dataset of over 500K labeled signals from over 100 individuals from 3
different wearable devices. We demonstrate that in comparison with a
traditional 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 through
convolutional denoising autoencoders dramatically improves the performance of
AF 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 derived
replication cohort of ambulatory subjects using data derived from an
independently engineered device. We show that two-stage training can help
address the unbalanced data problem common to biomedical applications where
large well-annotated datasets are scarce. In conclusion, though a combination
of simulation and transfer learning and we develop and apply a multitask
architecture to the problem of AF detection from wearable wrist sensors
demonstrating high levels of accuracy and a solution for the vexing challenge
of mechanical noise.
