Topological descriptors of foot clearance gait dynamics improve differential diagnosis of Parkinsonism
arXiv:2603.06212v1 Announce Type: new 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
arXiv:2603.06212v1 Announce Type: new 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.
Executive Summary
This article explores the application of Topological Data Analysis (TDA) to improve differential diagnosis of parkinsonian syndromes. By analyzing foot clearance gait dynamics, the study demonstrates the effectiveness of TDA in distinguishing between idiopathic Parkinson's disease (IPD) and vascular Parkinsonism (VaP). The results show that TDA-based features, particularly Betti-curve descriptors, can achieve high accuracy and area under the curve (AUC) values, especially when considering both 'On' and 'Off' states of medication. This approach has the potential to enhance clinical gait analysis and aid in the diagnosis of parkinsonian disorders.
Key Points
- ▸ TDA can effectively distinguish between IPD and VaP based on foot clearance gait dynamics
- ▸ Betti-curve descriptors yield the strongest results in classification models
- ▸ Performance improves when considering both 'On' and 'Off' states of medication
Merits
Novel Approach
The study introduces a novel application of TDA to gait analysis, offering a complementary tool for Parkinsonism classification.
High Accuracy
The results demonstrate high accuracy and AUC values, indicating the potential of TDA-based features in improving differential diagnosis.
Demerits
Small Sample Size
The study's sample size is relatively small, which may limit the generalizability of the findings.
Limited Comparison
The study primarily focuses on distinguishing between IPD and VaP, with limited comparison to other parkinsonian syndromes.
Expert Commentary
The application of TDA to gait analysis represents a significant advancement in the field, offering a more nuanced understanding of the complex patterns and structures underlying human movement. By leveraging the strengths of TDA and machine learning, researchers can develop more effective diagnostic tools and improve patient outcomes. However, further studies are needed to validate these findings and explore the broader applications of TDA in neurodegenerative diseases.
Recommendations
- ✓ Future studies should prioritize larger sample sizes and more comprehensive comparisons across parkinsonian syndromes
- ✓ The development of TDA-based tools for clinical gait analysis should be pursued, with a focus on translating research findings into practical applications
