Replication Data for: Automated classification of dystonia and choreoathetosis in dyskinetic cerebral palsy during a lower extremity task: a pilot study on a retrospective video dataset
Description
This RDR repository contains the data and code used in the study ‘Automated classification of dystonia in dyskinetic cerebral palsy within a lower extremity task using markerless motion tracking and time series classification: a pilot study on retrospective videos.’ The study had three main steps:
1. Kinematic Extraction with DeepLabCut 2.3 from videos [1, 2]
2. Post-processing of extracted X,Y coordinates
3. Time-Series Classification with HIVE-COTE 2.0 [3]. Users can begin with our preprocessing script or proceed directly to time series classification with the data provided.
Our raw data consisted of 66 videos from 33 participants (29 with dyskinetic cerebral palsy / four typically developing, age 7-23, 13 females / 20 males). The participants performed an item from the Dyskinesia Impairment Scale (DIS) - the heel-toe tapping task with the right lower leg (task 11) and the left lower leg (task 12) [4].
The original videos are NOT included in the RDR repro due to privacy reasons.
References:
1. Mathis, A., et al., DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat Neurosci, 2018. 21(9): p. 1281-1289.
2. github.com/DeepLabCut/DeepLabCut
3. Middlehurst M, Large J, Flynn M, Lines J, Bostrom A, Bagnall A. HIVE-COTE 2.0: a new meta ensemble for time series classification. Mach Learn . 2021 Dec 1; 110(11–12):3211–43
4. Monbaliu E, Ortibus E, de Cat J, Dan B, Heyrman L, Prinzie P, et al. The dyskinesia Impairment Scale: a new instrument to measure dystonia and choreoathetosis in dyskinetic cerebral palsy. Dev Med Child Neurol. 2012;54:278–83.
Resources
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Tags
- lower-extremity
- adolescent
- child
- time-series-analysis
- dystonia
- young-adult
- deep-learning-(machine-learning)
- cerebral-palsy,-dyskinetic
- athetosis
- chorea