Automated classification of neurological disorders of gait using spatio-temporal gait parameters.
OBJECTIVE
Automated pattern recognition systems have been used for accurate identification of neurological conditions as well as the evaluation of the treatment outcomes. This study aims to determine the accuracy of diagnoses of (oto-)neurological gait disorders using different types of automated pattern recognition techniques.


METHODS
Clinically confirmed cases of phobic postural vertigo (N = 30), cerebellar ataxia (N = 30), progressive supranuclear palsy (N = 30), bilateral vestibulopathy (N = 30), as well as healthy subjects (N = 30) were recruited for the study. 8 measurements with 136 variables using a GAITRite(®) sensor carpet were obtained from each subject. Subjects were randomly divided into two groups (training cases and validation cases). Sensitivity and specificity of k-nearest neighbor (KNN), naive-bayes classifier (NB), artificial neural network (ANN), and support vector machine (SVM) in classifying the validation cases were calculated.


RESULTS
ANN and SVM had the highest overall sensitivity with 90.6% and 92.0% respectively, followed by NB (76.0%) and KNN (73.3%). SVM and ANN showed high false negative rates for bilateral vestibulopathy cases (20.0% and 26.0%); while KNN and NB had high false negative rates for progressive supranuclear palsy cases (76.7% and 40.0%).


CONCLUSIONS
Automated pattern recognition systems are able to identify pathological gait patterns and establish clinical diagnosis with good accuracy. SVM and ANN in particular differentiate gait patterns of several distinct oto-neurological disorders of gait with high sensitivity and specificity compared to KNN and NB. Both SVM and ANN appear to be a reliable diagnostic and management tool for disorders of gait.
German Center for Vertigo and Balance Disorders, DSGZ, University of Munich, Campus Grosshadern, Marchioninistrasse 15, 81377 Munich, Germany. Electronic address: cauchypradhan@gmail.com.
Cauchy
C
Pradhan
German Center for Vertigo and Balance Disorders, DSGZ, University of Munich, Campus Grosshadern, Marchioninistrasse 15, 81377 Munich, Germany.
Max
M
Wuehr
German Center for Vertigo and Balance Disorders, DSGZ, University of Munich, Campus Grosshadern, Marchioninistrasse 15, 81377 Munich, Germany.
Farhoud
F
Akrami
German Center for Vertigo and Balance Disorders, DSGZ, University of Munich, Campus Grosshadern, Marchioninistrasse 15, 81377 Munich, Germany.
Maximilian
M
Neuhaeusser
German Center for Vertigo and Balance Disorders, DSGZ, University of Munich, Campus Grosshadern, Marchioninistrasse 15, 81377 Munich, Germany.
Sabrina
S
Huth
German Center for Vertigo and Balance Disorders, DSGZ, University of Munich, Campus Grosshadern, Marchioninistrasse 15, 81377 Munich, Germany; Institute of Clinical Neurosciences, University of Munich, Munich, Germany.
Thomas
T
Brandt
German Center for Vertigo and Balance Disorders, DSGZ, University of Munich, Campus Grosshadern, Marchioninistrasse 15, 81377 Munich, Germany; Department of Neurology, Schön Klinik Bad Aibling, 83043 Bad Aibling, Germany.
Klaus
K
Jahn
German Center for Vertigo and Balance Disorders, DSGZ, University of Munich, Campus Grosshadern, Marchioninistrasse 15, 81377 Munich, Germany; Department of Neurology, University of Munich, Campus Grosshadern, Marchioninistrasse 15, 81377 Munich, Germany.
Roman
R
Schniepp
D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D005260:Female; D005684:Gait / Q000502:physiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D018482:Muscle, Skeletal / Q000502:physiology*; D009422:Nervous System Diseases / Q000145:classification* / Q000175:diagnosis / Q000503:physiopathology*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis / Q000379:methods; D015203:Reproducibility of Results; D060388:Support Vector Machine; D013997:Time Factors
Artificial neural networks (ANN); GAITRite; Naive-bayes classifier (NB); Neurological disorders of gait; Pattern recognition; Support vector machines (SVM); k-nearest neighbor (KNN)