A Novel Video-Based Methodology for Automated Classification of Dystonia and Choreoathetosis in Dyskinetic Cerebral Palsy During a Lower Extremity Task.
Background: Movement disorders in children and adolescents with dyskinetic cerebral palsy (CP) are commonly assessed from video recordings, however scoring is time-consuming and expert knowledge is required for an appropriate assessment. Objective: To explore a machine learning approach for automate...
| Publicado en: | Neurorehabilitation & Neural Repair Vol. 38; no. 7; pp. 479 - 493 |
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| Autores principales: | , , , , , , , , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Jul2024
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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=177899885&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177899885 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15459683 IRK jtl: Neurorehabilitation & Neural Repair issn: 15459683 maglogo: Y pubinfo: dt: Jul2024 vid: 38 iid: 7 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 177899885 177665743 177899885 177899885 10.1177/15459683241257522 177899885 ppf: 479 ppct: 14 formats: tig: atl: A Novel Video-Based Methodology for Automated Classification of Dystonia and Choreoathetosis in Dyskinetic Cerebral Palsy During a Lower Extremity Task. aug: au: Haberfehlner, Helga Roth, Zachary Vanmechelen, Inti Buizer, Annemieke I. Jeroen Vermeulen, Roland Koy, Anne Aerts, Jean-Marie Hallez, Hans Monbaliu, Elegast affil: Department of Rehabilitation Sciences, KU Leuven Bruges, Bruges, Belgium sug: subj: Videorecording Automation Dystonia Classification Chorea Classification Dyskinesias Cerebral Palsy Lower Extremity Task Performance and Analysis Machine Learning Human Motion Analysis Systems Time Series Algorithms Scales Child Adolescence Descriptive Statistics Funding Source Child: 6-12 years Adolescent: 13-18 years ab: Background: Movement disorders in children and adolescents with dyskinetic cerebral palsy (CP) are commonly assessed from video recordings, however scoring is time-consuming and expert knowledge is required for an appropriate assessment. Objective: To explore a machine learning approach for automated classification of amplitude and duration of distal leg dystonia and choreoathetosis within short video sequences. Methods: Available videos of a heel-toe tapping task were preprocessed to optimize key point extraction using markerless motion analysis. Postprocessed key point data were passed to a time series classification ensemble algorithm to classify dystonia and choreoathetosis duration and amplitude classes (scores 0, 1, 2, 3, and 4), respectively. As ground truth clinical scoring of dystonia and choreoathetosis by the Dyskinesia Impairment Scale was used. Multiclass performance metrics as well as metrics for summarized scores: absence (score 0) and presence (score 1-4) were determined. Results: Thirty-three participants were included: 29 with dyskinetic CP and 4 typically developing, age 14 years:6 months ± 5 years:15 months. The multiclass accuracy results for dystonia were 77% for duration and 68% for amplitude; for choreoathetosis 30% for duration and 38% for amplitude. The metrics for score 0 versus score 1 to 4 revealed an accuracy of 81% for dystonia duration, 77% for dystonia amplitude, 53% for choreoathetosis duration and amplitude. Conclusions: This methodology study yielded encouraging results in distinguishing between presence and absence of dystonia, but not for choreoathetosis. A larger dataset is required for models to accurately represent distinct classes/scores. This study presents a novel methodology of automated assessment of movement disorders solely from video data. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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