Comparison among probabilistic neural network, support vector machine and logistic regression for evaluating the effect of subthalamic stimulation in Parkinson disease on ground reaction force during gait

• Published in: Journal of biomechanics Vol. 43; no. 4; pp. 720 - 726
• Main Authors: Muniz, A.M.S, Liu, H, Lyons, K.E, Pahwa, R, Liu, W, Nobre, F.F, Nadal, J
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 03-03-2010; Elsevier; Elsevier Limited
• Subjects: Agreements; Artificial Intelligence; Biological and medical sciences; Classification; Computerized, statistical medical data processing and models in biomedicine; Data Interpretation, Statistical; Deep brain stimulation; Degenerative and inherited degenerative diseases of the nervous system. Leukodystrophies. Prion diseases; Diagnosis, Computer-Assisted - methods; Diseases of the nervous system; Electric Stimulation Therapy - methods; Female; Foot - physiology; Gait; Gait analysis; Gait Disorders, Neurologic - diagnosis; Gait Disorders, Neurologic - etiology; Gait Disorders, Neurologic - physiopathology; Humans; Logistic Models; Logistic regression; Logistics; Male; Medical sciences; Middle Aged; Models and simulation; Neural networks; Neural Networks (Computer); Neurology; Parkinson disease; Parkinson Disease - complications; Parkinson Disease - physiopathology; Parkinson Disease - therapy; Pattern recognition; Personal relationships; Physical Medicine and Rehabilitation; Principal components analysis; Probabilistic neural network; Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects); Regression Analysis; Reproducibility of Results; Sensitivity and Specificity; Stress, Mechanical; Studies; Subthalamic Nucleus; Support vector machine; Therapy, Computer-Assisted - methods; Treatment Outcome; Variables; Parkinson disease; Logistic regression; Probabilistic neural network; Support vector machine; Deep brain stimulation; Gait analysis; Brain; Reaction force; Central nervous system; Modeling; Encephalon; Degenerative disease; Subthalamic nucleus; Biomedical engineering; Human; Nervous system diseases; Instrumentation therapy; Method; Neural network; Cerebral disorder; Biomechanics; Locomotion; Walking; Soils; Instrumental stimulation; Treatment; Central nervous system disease; Comparative study; Extrapyramidal syndrome
• ISSN: 0021-9290; 1873-2380
• DOI: 10.1016/j.jbiomech.2009.10.018

Abstract Deep brain stimulation of the subthalamic nucleus (DBS-STN) is an approved treatment for advanced Parkinson disease (PD) patients; however, there is a need to further evaluate its effect on gait. This study compares logistic regression (LR), probabilistic neural network (PNN) and support vector machine (SVM) classifiers for discriminating between normal and PD subjects in assessing the effects of DBS-STN on ground reaction force (GRF) with and without medication. Gait analysis of 45 subjects (30 normal and 15 PD subjects who underwent bilateral DBS-STN) was performed. PD subjects were assessed under four test conditions: without treatment (mof-sof), with stimulation alone (mof-son), with medication alone (mon-sof), and with medication and stimulation (mon-son). Principal component (PC) analysis was applied to the three components of GRF separately, where six PC scores from vertical, one from anterior–posterior and one from medial–lateral were chosen by the broken stick test. Stepwise LR analysis employed the first two and fifth vertical PC scores as input variables. Using the bootstrap approach to compare model performances for classifying GRF patterns from normal and untreated PD subjects, the first three and the fifth vertical PCs were attained as SVM input variables, while the same ones plus the first anterior–posterior were selected as PNN input variables. PNN performed better than LR and SVM according to area under the receiver operating characteristic curve and the negative likelihood ratio. When evaluating treatment effects, the classifiers indicated that DBS-STN alone was more effective than medication alone, but the greatest improvements occurred with both treatments together.