End-to-end Deep Learning from Raw Sensor Data: Atrial Fibrillation Detection using Wearables
This enables large-scale atrial fibrillation screenings for patients using wearable devices, representing a strong specific gain over previous feature-engineering methods.
The paper tackles atrial fibrillation detection from raw photoplethysmography data using an end-to-end convolutional-recurrent neural network, achieving state-of-the-art results with an area under ROC curve of 0.9999 and false positive/negative rates below 2×10^-3.
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.