Research Article: Gait phenotypes in paediatric hereditary spastic paraplegia revealed by dynamic time warping analysis and random forests

Date Published: March 8, 2018

Publisher: Public Library of Science

Author(s): Irene Pulido-Valdeolivas, David Gómez-Andrés, Juan Andrés Martín-Gonzalo, Irene Rodríguez-Andonaegui, Javier López-López, Samuel Ignacio Pascual-Pascual, Estrella Rausell, Christos Papadelis.


The Hereditary Spastic Paraplegias (HSP) are a group of heterogeneous disorders with a wide spectrum of underlying neural pathology, and hence HSP patients express a variety of gait abnormalities. Classification of these phenotypes may help in monitoring disease progression and personalizing therapies. This is currently managed by measuring values of some kinematic and spatio-temporal parameters at certain moments during the gait cycle, either in the doctor´s surgery room or after very precise measurements produced by instrumental gait analysis (IGA). These methods, however, do not provide information about the whole structure of the gait cycle. Classification of the similarities among time series of IGA measured values of sagittal joint positions throughout the whole gait cycle can be achieved by hierarchical clustering analysis based on multivariate dynamic time warping (DTW). Random forests can estimate which are the most important isolated parameters to predict the classification revealed by DTW, since clinicians need to refer to them in their daily practice. We acquired time series of pelvic, hip, knee, ankle and forefoot sagittal angular positions from 26 HSP and 33 healthy children with an optokinetic IGA system. DTW revealed six gait patterns with different degrees of impairment of walking speed, cadence and gait cycle distribution and related with patient’s age, sex, GMFCS stage, concurrence of polyneuropathy and abnormal visual evoked potentials or corpus callosum. The most important parameters to differentiate patterns were mean pelvic tilt and hip flexion at initial contact. Longer time of support, decreased values of hip extension and increased knee flexion at initial contact can differentiate the mildest, near to normal HSP gait phenotype and the normal healthy one. Increased values of knee flexion at initial contact and delayed peak of knee flexion are important factors to distinguish GMFCS stages I from II-III and concurrence of polyneuropathy.

Partial Text

The Hereditary Spastic Paraplegias (HSP) are a diverse, heterogeneous and large group of neurodegenerative and neurodevelopmental diseases, of which the main common feature is the retrograde degeneration of the cortico-spinal and posterior column pathways [1–3]. Patients with uncomplicated forms of HSP exhibit a combination of pyramidal syndrome and loss of proprioception, limited to or predominantly affecting the lower limbs. Patients with complicated forms of HSP manifest other associated neurological signs as a result of concomitant lesion of other parts of the nervous system (polyneuropathy, ataxia, mental retardation, epilepsy, dementia, extrapyramidal disorders, etc.)[1–3].

We have developed a new approach to determine and to understand gait patterns in HSP, which is a rare neurological disease. We have used a parameter-free classification approach for the whole gait cycle. We have used random forests to relate this classification to interpretable gait parameters that are widely used in clinical studies. This approach has revealed six main patterns in a sample of HSP children and has detected the important gait parameters that distinguish them. In addition, we have used random forests to discriminate between cycles from healthy children and those cycles from HSP children with milder gait abnormalities, and detected gait alterations while the disease remains with minimal expression. We have also related the above-mentioned gait patterns to age (as an indicator of disease duration), sex, GMFCS stage (as indicator of disease severity), presence of polyneuropathy, abnormal VEP and presence of thin corpus callosum. Finally, we have analyzed the effect of those clinical features on gait performance revealing specific gait abnormalities linked to each clinical item.




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