2 research outputs found
Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal
In this paper, we propose a novel contrastive learning based deep learning
framework for patient similarity search using physiological signals. We use a
contrastive learning based approach to learn similar embeddings of patients
with similar physiological signal data. We also introduce a number of neighbor
selection algorithms to determine the patients with the highest similarity on
the generated embeddings. To validate the effectiveness of our framework for
measuring patient similarity, we select the detection of Atrial Fibrillation
(AF) through photoplethysmography (PPG) signals obtained from smartwatch
devices as our case study. We present extensive experimentation of our
framework on a dataset of over 170 individuals and compare the performance of
our framework with other baseline methods on this dataset.Comment: 10 pages, 4 figures, Preprint submitted to Journal of Computers in
Biology and Medicin
BayesBeat: A Bayesian Deep Learning Approach for Atrial Fibrillation Detection from Noisy Photoplethysmography Data
The increasing popularity of smartwatches as affordable and longitudinal
monitoring devices enables us to capture photoplethysmography (PPG) sensor data
for detecting Atrial Fibrillation (AF) in real-time. A significant challenge in
AF detection from PPG signals comes from the inherent noise in the smartwatch
PPG signals. In this paper, we propose a novel deep learning based approach,
BayesBeat that leverages the power of Bayesian deep learning to accurately
infer AF risks from noisy PPG signals, and at the same time provide the
uncertainty estimate of the prediction. Bayesbeat is efficient, robust,
flexible, and highly scalable which makes it particularly suitable for
deployment in commercially available wearable devices. Extensive experiments on
a recently published large dataset reveal that our proposed method BayesBeat
substantially outperforms the existing state-of-the-art methods.Comment: 8 pages, 5 figure