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Windowed persistent homology: A topological signal processing algorithm applied to clinical obesity data.

Craig Biwer1, Amy Rothberg2, Heidi IglayReger2

  • 1Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States of America.

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Summary

Predicting weight loss success is crucial for managing obesity. New signal processing methods using windowed persistent homology and Hausdorff distance can now differentiate patients likely to maintain weight loss from those who will regain it.

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Area of Science:

  • Biomedical Engineering
  • Data Science
  • Clinical Informatics

Background:

  • Overweight and obesity affect two-thirds of Americans, posing a significant healthcare burden.
  • Predicting weight loss maintenance is challenging, with common methods often failing to identify patient outcomes.
  • Ineffective treatments due to poor prediction lead to increased healthcare costs and wasted effort.

Purpose of the Study:

  • To develop and validate a novel signal processing approach for predicting weight loss maintenance.
  • To differentiate patients prone to weight regain from those who successfully maintain weight loss.
  • To improve clinical decision-making and patient care strategies for obesity management.

Main Methods:

  • Introduction of a novel windowed persistent homology algorithm.
  • Application of a modified, semimetric Hausdorff distance for data comparison.
  • Testing the novel approach on accelerometer data from an ongoing study.

Main Results:

  • The windowed persistent homology and modified Hausdorff semimetric approach successfully differentiated between weight loss maintainers and those prone to regain.
  • Standard signal processing methods failed to show a significant difference between the two patient groups.
  • The novel method demonstrated a clear separation, unlike conventional techniques.

Conclusions:

  • The developed signal processing technique offers a promising tool for predicting weight loss outcomes.
  • This approach has significant implications for personalizing obesity treatment and improving patient care.
  • The findings suggest a potential shift in clinical decision-making for weight management interventions.