Machine learning model comparison for freezing of gait prediction in advanced Parkinson's disease.
Introduction: Freezing of gait (FOG) is a paroxysmal motor phenomenon that increases in prevalence as Parkinson's disease (PD) progresses. It is associated with a reduced quality of life and an increased risk of falls in this population. Precision-based detection and classification of freezers are c...
| Publicado en: | Frontiers in Aging Neuroscience pp. 1 - 7 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Frontiers Media S.A.
2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=178434092&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178434092 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16634365 BG2U jtl: Frontiers in Aging Neuroscience issn: 16634365 maglogo: N pubinfo: dt: 2024 pid: 40038 pub: Frontiers Media S.A. artinfo: ui: 178434092 178434092 178434092 10.3389/fnagi.2024.1431280 178434092 ppf: 1 ppct: 6 formats: tig: atl: Machine learning model comparison for freezing of gait prediction in advanced Parkinson's disease. aug: au: Watts, Jeremy Niethammer, Martin Khojandi, Anahita Ramdhani, Ritesh affil: Department of Mathematics, University of Tennessee, Knoxville, TN, United States sug: subj: Parkinson Disease Machine Learning Kinematics Evaluation Gait Disorders, Neurologic Quality of Life Accidental Falls Risk Factors Risk Assessment Prediction Models Human Male Female Middle Age Aged Aged, 80 and Over Descriptive Statistics Algorithms Deep Brain Stimulation Wearable Sensors Range of Motion Gait Analysis Funding Source Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Introduction: Freezing of gait (FOG) is a paroxysmal motor phenomenon that increases in prevalence as Parkinson's disease (PD) progresses. It is associated with a reduced quality of life and an increased risk of falls in this population. Precision-based detection and classification of freezers are critical to developing tailored treatments rooted in kinematic assessments. Methods: This study analyzed instrumented stand-and-walk (SAW) trials from advanced PD patients with STN-DBS. Each patient performed two SAW trials in their OFF Medication--OFF DBS state. For each trial, gait summary statistics from wearable sensors were analyzed by machine learning classification algorithms. These algorithms include k-nearest neighbors, logistic regression, naïve Bayes, random forest, and support vector machines (SVM). Each of these models were selected for their high interpretability. Each algorithm was tasked with classifying patients whose SAW trials MDS-UPDRS FOG subscore was non-zero as assessed by a trained movement disorder specialist. These algorithms' performance was evaluated using stratified five-fold cross-validation. Results: A total of 21 PD subjects were evaluated (average age 64.24 years, 16 males, mean disease duration of 14 years). Fourteen subjects had freezing of gait in the OFF MED/OFF DBS. All machine learning models achieved statistically similar predictive performance (p < 0.05) with high accuracy. Analysis of random forests' feature estimation revealed the top-ten spatiotemporal predictive features utilized in the model: foot strike angle, coronal range of motion [trunk and lumbar], stride length, gait speed, lateral step variability, and toe-off angle. Conclusion: These results indicate that machine learning effectively classifies advanced PD patients as freezers or nonfreezers based on SAW trials in their non-medicated/non-stimulated condition. The machine learning models, specifically random forests, not only rely on but utilize salient spatial and temporal gait features for FOG classification. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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