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A versatile computational algorithm for time-series data analysis and machine-learning models.

Taylor Chomiak1,2, Neilen P Rasiah3, Leonardo A Molina4

  • 1Division of Translational Neuroscience, Department of Clinical Neurosciences, Hotchkiss Brain Institute, Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive, Calgary, AB, T2N 4N1, Canada. tgchomia@ucalgary.ca.

NPJ Parkinson'S Disease
|November 10, 2021
PubMed
Summary
This summary is machine-generated.

We developed Local Topological Recurrence Analysis (LoTRA), a new computational method for time-series data. LoTRA can detect Parkinson's disease from handwriting, outperforming complex deep-learning models.

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Area of Science:

  • Computational neuroscience
  • Data analysis
  • Machine learning

Background:

  • Time-series data analysis is crucial in many scientific fields.
  • Existing methods may lack simplicity or generalizability.
  • Detecting neurodegenerative diseases like Parkinson's often requires complex diagnostics.

Purpose of the Study:

  • To introduce Local Topological Recurrence Analysis (LoTRA), a novel computational approach for time-series analysis.
  • To demonstrate the versatility and efficacy of LoTRA across diverse datasets.
  • To develop a simple machine learning model using LoTRA for early Parkinson's disease detection.

Main Methods:

  • Developed the Local Topological Recurrence Analysis (LoTRA) algorithm.
  • Applied LoTRA to simulated time-series data.
  • Utilized LoTRA to analyze Parkinsonian gait and in vivo brain dynamics.
  • Constructed a machine learning model based on LoTRA for disease detection.

Main Results:

  • LoTRA effectively analyzes diverse time-series data, including simulated data, gait patterns, and neural activity.
  • A machine learning model using LoTRA achieved superior performance in detecting Parkinson's disease compared to deep-learning models.
  • The LoTRA-based model successfully identified Parkinson's disease from a single digital handwriting test.

Conclusions:

  • Local Topological Recurrence Analysis (LoTRA) is a versatile and powerful tool for time-series analysis.
  • LoTRA enables the development of simple yet highly effective machine learning models.
  • This approach offers a promising, accessible method for early Parkinson's disease detection using digital handwriting.