ANNOTE: Annotation of time-series events

Groh R, Li JY, Li-Jessen NY, Kist A (2024)

Publication Type: Journal article

Publication year: 2024

Journal

Software Impacts Elsevier

Book Volume: 21

Article Number: 100679

DOI: 10.1016/j.simpa.2024.100679

Abstract

Supervised training of machine learning models heavily relies on accurate annotations. However, data annotation, such as in the case of time-series signals, poses a labor-intensive challenge. Here, we present a new annotation software, Annotation of Time-series Events (ANNOTE), to handle longitudinal, time-series signals as in highly complex physiological events. ANNOTE offers flexibility and adaptability to streamline the annotation process through an intuitive user interface, effectively meeting diverse annotation needs. Users can annotate regions of interest with precision down to a single data point. ANNOTE presents a useful tool to support researchers in handling time-series biomedical data for downstream machine-learning analyses.

Authors with CRIS profile

René Groh Juniorprofessur für Artificial Intelligence in Communication Disorders Andreas Kist Juniorprofessur für Artificial Intelligence in Communication Disorders

Involved external institutions

McGill University

Canada (CA)

How to cite

APA:

Groh, R., Li, J.Y., Li-Jessen, N.Y., & Kist, A. (2024). ANNOTE: Annotation of time-series events. Software Impacts, 21. https://doi.org/10.1016/j.simpa.2024.100679

MLA:

Groh, René, et al. "ANNOTE: Annotation of time-series events." Software Impacts 21 (2024).

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