DOSE-I: A Multimodal Biosignal Dataset of Procedural Sedation for Endoscopy -- Technical Report

2026-06-30

AI Summary

Existing research on endoscopic sedation is hindered by the lack of high-quality, multimodal physiological signal datasets with fine-grained annotations of consciousness states. To address this gap, this work introduces and publicly releases the DOSE-I dataset, comprising 171 multimodal physiological recordings totaling 78.5 hours from 281 endoscopic procedures. It is the first to systematically integrate large-scale clinical multichannel biosignals with expert-annotated temporal transitions of consciousness (1,129 events) and sedation depth labels (7,328 instances). The dataset also includes subject demographics, metadata, artifact detection results, and C-implemented preprocessing code for pEEG features. This comprehensive resource substantially advances reproducible research in quantitative sedation depth assessment and dynamic modeling of consciousness.

Abstract

In this document, we describe characteristics and technical details of the multimodal biosignal dataset DOSE-I of procedural sedation for endoscopy published on zenodo. The DOSE-I dataset includes 78.5 hours of recording in 171 records ranging from 6.7 to 70.8 minutes (mean: 27.5, SD: 11.6) of 281 endoscopic procedures. 1129 (median: 6 per record) transitions of consciousness and 7328 (median: 39 per record) individual sedation depth labels were recorded. In addition to clinically annotated biosignals, the DOSE-I dataset provides detailed static data about the respective study subject and metadata about the respective recordings. To further support future research, we provide details about artifact detection and preprocessed pEEG features, too. C code used for this preprocessing is provided separately via Github.

Problem

• procedural sedation
• endoscopy
• multimodal biosignal dataset
• sedation depth
• consciousness transition

Innovation

• multimodal biosignal dataset
• procedural sedation
• consciousness transition
• sedation depth labeling
• pEEG preprocessing