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Related Experiment Videos

Using gait analysis' parameters to classify Parkinsonism: A data mining approach.

Carlo Ricciardi1, Marianna Amboni2, Chiara De Santis3

  • 1Department of Advanced Biomedical Sciences, University Hospital of Naples 'Federico II', Via S. Pansini, 5, Naples 80131, Italy; Istituti Clinici Scientifici Maugeri IRCCS, Via bagni vecchi, 1, Telese Terme (BN), Italy.

Computer Methods and Programs in Biomedicine
|August 25, 2019
PubMed

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Summary
This summary is machine-generated.

Data mining of gait analysis effectively distinguishes Parkinson's disease (PD) from Progressive Supranuclear Palsy (PSP). This approach aids early diagnosis when differentiating these neurodegenerative conditions is challenging.

Area of Science:

  • Neurology
  • Biomedical Engineering
  • Data Science

Background:

  • Parkinson's disease (PD) and Progressive Supranuclear Palsy (PSP) are neurodegenerative disorders with overlapping early symptoms.
  • Gait dysfunction is a key motor symptom in both PD and PSP, offering potential for differential diagnosis.
  • Subtle gait differences may be detectable through advanced analysis, aiding early clinical differentiation.

Purpose of the Study:

  • To apply data mining techniques to gait analysis parameters for classifying PD and PSP patients.
  • To investigate the utility of spatial and temporal gait data in distinguishing early-stage PD from PSP.
  • To develop a data-driven tool for aiding clinicians in differential diagnosis.

Main Methods:

  • A cohort of 46 subjects with PD (various stages) and PSP underwent gait analysis.
Keywords:
Data miningGait analysisGradient boosted treesParkinson's diseaseProgressive supranuclear palsyRandom forests

Related Experiment Videos

  • Spatial and temporal gait parameters were analyzed using data mining algorithms.
  • Synthetic Minority Over-sampling Technique and cross-validation were employed to manage dataset imbalance and ensure robust evaluation.
  • Random Forests and Gradient Boosted Trees algorithms were implemented for classification.

    • Main Results:

    • Random Forests achieved the highest classification accuracy at 86.4%.
    • Both Random Forests and Gradient Boosted Trees demonstrated high specificity and sensitivity, exceeding 90% for the PSP group.
    • The analysis utilized 16 distinct gait features.

    Conclusions:

    • Data mining of gait analysis parameters offers a novel approach to differentiate typical Parkinson's disease (PD) from atypical Parkinsonism (PSP).
    • This method can assist clinicians in distinguishing PSP from PD, particularly during early disease stages when diagnosis is most challenging.
    • Gait pattern analysis holds significant potential for improving the accuracy of differential diagnosis in neurodegenerative movement disorders.