PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation
Maxwell Xu, Alexander Moreno, Supriya Nagesh, Varol Burak Aydemir, David W Wetter, Santosh Kumar, James Matthew Rehg
Abstract
The promise of Mobile Health (mHealth) is the ability to use wearable sensors to monitor participant physiology at high frequencies during daily life to enable temporally-precise health interventions. However, a major challenge is frequent missing data. Despite a rich imputation literature, existing techniques are ineffective for the pulsative signals which comprise many mHealth applications, and a lack of available datasets has stymied progress. We address this gap with PulseImpute, the first large-scale pulsative signal imputation challenge which includes realistic mHealth missingness models, an extensive set of baselines, and clinically-relevant downstream tasks. Our baseline models include a novel transformer-based architecture designed to exploit the structure of pulsative signals. We hope that PulseImpute will enable the ML community to tackle this important and challenging task.
BibTeX
@inproceedings{
xu2022pulseimpute,
title={PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation},
author={Maxwell Xu and Alexander Moreno and Supriya Nagesh and Varol Burak Aydemir and David W Wetter and Santosh Kumar and James Matthew Rehg},
booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2022},
url={https://openreview.net/forum?id=x_kBZYiUrxR}
}