# End-to-end Deep Learning from Raw Sensor Data: Atrial Fibrillation Detection using Wearables

arXiv: https://arxiv.org/abs/1807.10707 · HTML: https://mlsanity.com/paper/1807.10707/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2018.

## Built on by (most cited first, 4 of 4)

- [SiamAF: Learning Shared Information from ECG and PPG Signals for Robust Atrial Fibrillation Detection](https://mlsanity.com/paper/2310.09203/) (2023)
- [Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal](https://mlsanity.com/paper/2308.02433/) (2023)
- [A systematic evaluation of uncertainty quantification techniques in deep learning: a case study in photoplethysmography signal analysis](https://mlsanity.com/paper/2511.00301/) (2025)
- [Deep Learning Classification of Photoplethysmogram Signal for Hypertension Levels](https://mlsanity.com/paper/2405.14556/) (2024)

## Abstract (arXiv)

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× 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.