aiqbench analysis
This technical report introduces the DOSE-I dataset, a large-scale collection of multimodal biosignals designed to advance AI models for predicting sedation depth during endoscopic procedures. By providing preprocessed pEEG features and artifact detection tools alongside detailed clinical annotations, it offers critical infrastructure for training robust machine learning systems in medical monitoring contexts.
DOSE-I: A Multimodal Biosignal Dataset of Procedural Sedation for Endoscopy -- Technical Report
arXiv:2607.02570v2 Announce Type: replace-cross 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.
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.
Dataset can be accessed via zenodo DOI this https URL For citation use the primary academic reference: Garbe J et al. Towards predicting sedation depth in endoscopy with large clinically annotated EEG data of continuous Propofol sedation. In: P. Andreevetal (Eds.): AIME2026, LNAI 16749, p.1-6, Springer, 2026. this https URL
Signal Processing (eess.SP); Artificial Intelligence (cs.AI)
arXiv:2607.02570 [eess.SP]
(or arXiv:2607.02570v2 [eess.SP] for this version)
Full text under the publisher's open licence · 183 words
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