Gait disorders are common in neurodegenerative diseases and distinguishing between
seemingly similar kinematic patterns associated with different pathological entities is a
challenge even for the experienced clinician. Ultimately, muscle activity underlies the
generation of kinematic patterns. Therefore, one possible way to address this problem
may be to differentiate gait disorders by analyzing intrinsic features of muscle activations
patterns. Here, we examined whether it is possible to differentiate electromyography
(EMG) gait patterns of healthy subjects and patients with different gait disorders using
machine learning techniques. Nineteen healthy volunteers (9 male, 10 female, age 28.2
± 6.2 years) and 18 patients with gait disorders (10 male, 8 female, age 66.2 ± 14.7
years) resulting from different neurological diseases walked down a hallway 10 times at
a convenient pace while their muscle activity was recorded via surface EMG electrodes
attached to 5 muscles of each leg (10 channels in total). Gait disorders were classified
as predominantly hypokinetic (n = 12) or ataxic (n = 6) gait by two experienced raters
based on video recordings. Three different classification methods (Convolutional Neural
Network—CNN, Support Vector Machine—SVM, K-Nearest Neighbors—KNN) were
used to automatically classify EMG patterns according to the underlying gait disorder
and differentiate patients and healthy participants. Using a leave-one-out approach for
training and evaluating the classifiers, the automatic classification of normal and abnormal
EMG patterns during gait (2 classes: “healthy” and “patient”) was possible with a high
degree of accuracy using CNN (accuracy 91.9%), but not SVM (accuracy 67.6%) or
KNN (accuracy 48.7%). For classification of hypokinetic vs. ataxic vs. normal gait (3
classes) best results were again obtained for CNN (accuracy 83.8%) while SVM and
KNN performed worse (accuracy SVM 51.4%, KNN 32.4%). These results suggest that
machine learning methods are useful for distinguishing individuals with gait disorders
from healthy controls and may help classification with respect to the underlying disorder
even when classifiers are trained on comparably small cohorts. In our study, CNN
achieved higher accuracy than SVM and KNN and may constitute a promising method
for further investigation.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:84334 |
| Date | 27 March 2023 |
| Creators | Fricke, Christopher, Alizadeh, Jalal, Zakhary, Nahrin, Woost, Timo B., Bogdan, Martin, Classen, Joseph |
| Publisher | Frontiers Research Foundation |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:article, info:eu-repo/semantics/article, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 1664-2295, 666458 |
Page generated in 0.0021 seconds