Technology Category
Machine Learning: Learning with ManifoldsData Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal DataPlanning, Routing, and Scheduling: Activity and Plan Recognition
Topological descriptors of foot clearance gait dynamics improve differential diagnosis of Parkinsonism
🤖 AI Summary
This study addresses the clinical challenge of differentiating Parkinsonian subtypes, which is complicated by overlapping motor symptoms and subtle gait abnormalities. For the first time, topological data analysis (TDA) is applied to Parkinsonian gait research, leveraging persistent homology to extract topological features—such as Betti curves, persistence landscapes, and persistence entropy—from foot clearance time series. These features effectively capture nonlinear dynamic structures overlooked by conventional methods and demonstrate sensitivity to levodopa medication status. When integrated with a random forest classifier, the approach achieves 83% accuracy (AUC = 0.89) in distinguishing idiopathic Parkinson’s disease from vascular Parkinsonism under the on-medication condition. Performance further improves when combining both on- and off-medication data, highlighting the potential of TDA as a sensitive and discriminative tool for Parkinsonian subtype classification.
Technology Category
Application Category
📝 Abstract
Differential diagnosis among parkinsonian syndromes remains a clinical challenge due to overlapping motor symptoms and subtle gait abnormalities. Accurate differentiation is crucial for treatment planning and prognosis. While gait analysis is a well established approach for assessing motor impairments, conventional methods often overlook hidden nonlinear and structural features embedded in foot clearance patterns. We evaluated Topological Data Analysis (TDA) as a complementary tool for Parkinsonism classification using foot clearance time series. Persistent homology produced Betti curves, persistence landscapes, and silhouettes, which were used as features for a Random Forest classifier. The dataset comprised 15 controls (CO), 15 idiopathic Parkinson's disease (IPD), and 14 vascular Parkinsonism (VaP). Models were assessed with leave-one-out cross-validation (LOOCV). Betti-curve descriptors consistently yielded the strongest results. For IPD vs VaP, foot clearance variables minimum toe clearance, maximum toe late swing, and maximum heel clearance achieved 83% accuracy and AUC=0.89 under LOOCV in the medicated (On) state. Performance improved in the On state and further when both Off and On states were considered, indicating sensitivity of the topological features to levodopa related gait changes. These findings support integrating TDA with machine learning to improve clinical gait analysis and aid differential diagnosis across parkinsonian disorders.
Problem
Research questions and friction points this paper is trying to address.
Parkinsonism
differential diagnosis
gait analysis
foot clearance
motor symptoms
Innovation
Methods, ideas, or system contributions that make the work stand out.
Topological Data Analysis
Persistent Homology
Gait Dynamics
Parkinsonism Differential Diagnosis
Machine Learning
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2024-05-21arXiv.orgCitations: 2
