ANNOTE: Annotation of time-series events - Groh, René; Li, Jie Yu; Li-Jessen, Nicole Y.K.; Kist, Andreas (Software Impacts, 2024)
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.
CRIS-Team Scopus Importer:2024-07-19
Keywords: Annotation; Multi-channel; Time-series labeling