  We present a convolutional-recurrent neural network architecture with long
short-term memory for real-time processing and classification of digital sensor
data. The network implicitly performs typical signal processing tasks such as
filtering and peak detection, and learns time-resolved embeddings of the input
signal. We use a prototype multi-sensor wearable device to collect over 180h of
photoplethysmography (PPG) data sampled at 20Hz, of which 36h are during atrial
fibrillation (AFib). We use end-to-end learning to achieve state-of-the-art
results in detecting AFib from raw PPG data. For classification labels output
every 0.8s, we demonstrate an area under ROC curve of 0.9999, with false
positive and false negative rates both below $2\times 10^{-3}$. This
constitutes a significant improvement on previous results utilising
domain-specific feature engineering, such as heart rate extraction, and brings
large-scale atrial fibrillation screenings within imminent reach.
