PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation

Maxwell Xu · Alexander Moreno · Supriya Nagesh · Varol Aydemir · David Wetter · Santosh Kumar · James Rehg

Hall J #1022

Keywords: [ missingness ] [ mHealth ] [ sensors ] [ physiological ] [ pulsative ] [ time-series ] [ imputation ] [ dataset ] [ self-attention ]

[ Abstract ]
[ Paper [ Poster [ OpenReview
Tue 29 Nov 2 p.m. PST — 4 p.m. PST


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.

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