Keywords: missingness, imputation, mHealth, sensors, time-series, self-attention, pulsative, physiological, dataset
TL;DR: PulseImpute is the first mHealth pulsative signal imputation challenge which includes realistic missingness models, clinical downstream tasks, and an extensive set of baselines, including an augmented transformer that achieves SOTA performance.
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.
Supplementary Material: pdf
Contribution Process Agreement: Yes
In Person Attendance: Yes
Dataset Url: Code URL: www.github.com/rehg-lab/pulseimpute Data URL: www.doi.org/10.5281/zenodo.7129965
License: Code License: MIT License Data License: Creative Commons Attribution 4.0 International
Author Statement: Yes