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Related Experiment Videos

Estimating false-positive and false-negative errors in analyses of hormonal pulsatility.

E Van Cauter1

  • 1Institute of Interdisciplinary Research, Free University of Brussels, Belgium.

The American Journal of Physiology
|June 1, 1988
PubMed
Summary

Computer algorithms for endocrine pulse detection may underestimate pulse frequency. Minimizing false positives in noisy data can increase false negatives, especially with frequent pulses.

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Area of Science:

  • Endocrinology
  • Computational Biology
  • Signal Processing

Background:

  • Previous studies focused on minimizing false positives in random noise for endocrine pulse detection algorithms.
  • The ULTRA algorithm identifies peaks based on local intra-assay coefficient of variation thresholds.

Purpose of the Study:

  • To investigate the trade-off between false-positive and false-negative rates in endocrine pulse detection.
  • To evaluate how noise, pulse frequency, amplitude, and baseline variations affect detection errors.

Main Methods:

  • Analysis of 336 computer-generated series with varying parameters.
  • Utilized the ULTRA algorithm with thresholds of two and three coefficients of variation.

Main Results:

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  • False-positive rates were significantly higher (4-10x) in series with pulses compared to pure noise (≥8 pulses/100 samples).
  • Increasing pulse frequency reduced false positives but increased false negatives.
  • Thresholds minimizing false positives in noise led to >20% false negatives with ≥8 pulses/100 samples.

Conclusions:

  • Pulse detection criteria optimized for low false positives in noise can lead to underestimation of pulse frequency in hormonal profiles.
  • Careful threshold selection is crucial to balance false positives and false negatives in endocrine signal analysis.