Automated classification of neurological disorders of gait using spatio-temporal gait parameters

• Published in: Journal of electromyography and kinesiology Vol. 25; no. 2; pp. 413 - 422
• Main Authors: Pradhan, Cauchy, Wuehr, Max, Akrami, Farhoud, Neuhaeusser, Maximilian, Huth, Sabrina, Brandt, Thomas, Jahn, Klaus, Schniepp, Roman
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.04.2015
• Subjects: Aged; Aged, 80 and over; Algorithms; Artificial Intelligence; Artificial neural networks (ANN); Bayes Theorem; Female; Gait - physiology; GAITRite; Humans; k-nearest neighbor (KNN); Male; Middle Aged; Muscle, Skeletal - physiology; Naive-bayes classifier (NB); Nervous System Diseases - classification; Nervous System Diseases - diagnosis; Nervous System Diseases - physiopathology; Neural Networks (Computer); Neurological disorders of gait; Pattern recognition; Pattern Recognition, Automated - methods; Physical Medicine and Rehabilitation; Principal Component Analysis - methods; Reproducibility of Results; Support Vector Machine; Support vector machines (SVM); Time Factors; Neurological disorders of gait; k-nearest neighbor (KNN); Naive-bayes classifier (NB); Pattern recognition; Artificial neural networks (ANN); GAITRite; Support vector machines (SVM)
• ISSN: 1050-6411; 1873-5711
• DOI: 10.1016/j.jelekin.2015.01.004

Abstract 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.