PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation

Maxwell Xu, Alexander Moreno, Supriya Nagesh, V Burak Aydemir, David Wetter, Santosh Kumar, James M Rehg

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish (US)
Title of host publication36th Conference on Neural Information Processing Systems, NeurIPS 2022
EditorsS. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh
PublisherCurran Associates Inc.
Pages26874-26888
Number of pages15
ISBN (Electronic)9781713871088
StatePublished - 2022
Externally publishedYes

Publication series

NameAdvances in Neural Information Processing Systems
Volume35
ISSN (Print)1049-5258

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