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Evaluating interactive 2D visualization as a sample selection strategy for biomedical time-series data annotation.

Einari Vaaras1, Manu Airaksinen2, Okko Räsänen1

  • 1Signal Processing Research Centre, Tampere University, Tampere, 33720, Finland.

Computers in Biology and Medicine
|June 16, 2026
PubMed
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Comparing annotation methods for biomedical time-series data, 2D visualizations (2DV) generally improved label aggregation. However, random sampling (RND) offered the safest approach with uncertain annotator expertise or count.

Area of Science:

  • Biomedical Informatics
  • Machine Learning
  • Data Science

Background:

  • Accurate data labeling is crucial for reliable machine learning in biomedical applications.
  • Annotating complex biomedical time-series data presents significant challenges.
  • Algorithmic sample selection methods can aid annotation but require validation with human annotators.

Purpose of the Study:

  • To compare the effectiveness of three algorithmic sample selection methods for annotating biomedical time-series data.
  • To evaluate random sampling (RND), farthest-first traversal (FAFT), and a 2D visualization (2DV) based method.
  • To assess performance across infant motility assessment (IMA) and speech emotion recognition (SER) tasks with varying annotator expertise.

Main Methods:

  • Three sample selection methods (RND, FAFT, 2DV) were compared across four classification tasks.
Keywords:
2D visualizationBiomedical time-series data annotationHuman activity recognitionMulti-sensor inertial measurement unitNeonatal intensive care unitSample selectionSpeech emotion recognition

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  • Twelve human annotators (experts and non-experts) performed data annotation under a limited budget.
  • Post-annotation experiments evaluated method performance and annotator experience.

    • Main Results:

    • The 2DV method generally yielded the best aggregated labels across all tasks.
    • In infant motility assessment, 2DV captured rare classes but showed high label variability, favoring FAFT for individual annotator models.
    • For speech emotion recognition, 2DV excelled with expert annotators and matched expert performance with non-experts.
    • Random sampling (RND) presented the lowest risk when annotator count or expertise was uncertain.

    Conclusions:

    • 2D visualization-based sampling is a promising approach for biomedical time-series data annotation, especially with adequate annotation budgets.
    • The choice of method depends on task specifics, annotator characteristics, and budget constraints.
    • The 2DV method enhanced annotator engagement, making the task more interesting and enjoyable.