PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation

• URL: http://arxiv.org/abs/2212.07514v2
• Date: Fri, 15 Dec 2023 17:45:51 GMT
• Title: PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation
• Authors: Maxwell A. Xu, Alexander Moreno, Supriya Nagesh, V. Burak Aydemir, David W. Wetter, Santosh Kumar, James M. Rehg
• Abstract summary: 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. Despite a rich imputation literature, existing techniques are ineffective for the pulsative signals which comprise many mHealth applications. 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.
• Score: 54.839600943189915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 significant and challenging task.

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