PaperarXiv:2307.05385
Sparse learned kernels for interpretable and efficient medical time series processing
Nature Machine Intelligence
TL;DR
Single-layer sparse mixture of learned 1-D kernels for PPG artifact segmentation (and a classification head for ECG AF). Trained on PPG-DaLiA; tested on DaLiA / WESAD / TROIKA.
- Form factor: wrist
Abstract
Rapid, reliable, and accurate interpretation of medical time-series signals is crucial for high-stakes clinical decision-making. Deep learning methods offered unprecedented performance in medical signal processing but at a cost: they were compute-intensive and lacked interpretability. We propose Sparse Mixture of Learned Kernels (SMoLK), an interpretable architecture for medical time series processing. SMoLK learns a set of lightweight flexible kernels that form a single-layer sparse neural network, providing not only interpretability, but also efficiency, robustness, and generalization to unseen data distributions. We introduce a parameter reduction techniques to reduce the size of SMoLK's networks while maintaining performance. We test SMoLK on two important tasks common to many consumer wearables: photoplethysmography (PPG) artifact detection and atrial fibrillation detection from single-lead electrocardiograms (ECGs). We find that SMoLK matches the performance of models orders of magnitude larger. It is particularly suited for real-time applications using low-power devices, and its interpretability benefits high-stakes situations.
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Cite this paper
Chen, S. F., Guo, Z., Ding, C., Hu, X., & Rudin, C. (2024). Sparse learned kernels for interpretable and efficient medical time series processing. Nature Machine Intelligence. https://doi.org/10.1038/s42256-024-00898-4
Chen, Sully F., Zhicheng Guo, Cheng Ding, Xiao Hu, and Cynthia Rudin. 2024. "Sparse learned kernels for interpretable and efficient medical time series processing." Nature Machine Intelligence. https://doi.org/10.1038/s42256-024-00898-4.
Chen, S. F., Guo, Z., Ding, C., Hu, X. and Rudin, C. (2024) 'Sparse learned kernels for interpretable and efficient medical time series processing', Nature Machine Intelligence. doi: 10.1038/s42256-024-00898-4.
Chen, Sully F., et al. "Sparse learned kernels for interpretable and efficient medical time series processing." Nature Machine Intelligence, 2024. https://doi.org/10.1038/s42256-024-00898-4.
S. F. Chen, Z. Guo, C. Ding, X. Hu, and C. Rudin, "Sparse learned kernels for interpretable and efficient medical time series processing," Nature Machine Intelligence, 2024, doi: 10.1038/s42256-024-00898-4.
@article{chen2024sparse,
title = {{Sparse learned kernels for interpretable and efficient medical time series processing}},
author = {Chen, Sully F. and Guo, Zhicheng and Ding, Cheng and Hu, Xiao and Rudin, Cynthia},
year = {2024},
journal = {Nature Machine Intelligence},
doi = {10.1038/s42256-024-00898-4},
eprint = {2307.05385},
archivePrefix = {arXiv}
}Added to the ChatPPG research hub
