مقایسه میان شبکه احتمالاتی عصبی، ماشین بردار پشتیبانی و رگرسیون لجستیک برای ارزیابی اثر تحریک تالاموس در بیماری پارکینسون در نیروی عکس العمل زمین در طی راه رفتن
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|24842||2010||7 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Biomechanics, Volume 43, Issue 4, 3 March 2010, Pages 720–726
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.
Parkinson disease (PD) is a neurodegenerative disorder leading to difficulty in motor function, including gait and balance. Deep brain stimulation of the subthalamic nucleus (DBS-STN) is a treatment for advanced PD patients with disabling motor fluctuations, allowing a significant reduction in dopaminergic medications (Ferrarin et al., 2005). Various studies have evaluated the effects of DBS-STN using clinical motor scores (Krack et al., 2003; Ostergaard and Sundae, 2006), while only a few have quantitatively assessed the gait of PD patients (Liu et al., 2005; Ferrarin et al., 2005). Gait speed is shown to be the variable most affected by the DBS-STN; however, it does not take into account atypical waveforms and therefore does not provide enough information about the gait pattern (Schwartz and Rozumalski, 2008). Approaches that capture features of the entire waveform instead of a few parameters may improve the effectiveness of the analysis (Chester et al., 2007). Additionally, the correlations among variables must be considered to accurately evaluate the extent of gait abnormalities and to assess the changes resulting from a specific treatment (Schutte et al., 2000). A clinical challenge is to understand the disease process as well as outcomes of potential interventions. Logistic regression (LR) is commonly used as a linear predictive model for diagnostic and prognostic tasks. Recently, computational intelligence techniques such as artificial neural networks (ANN) and support vector machines (SVM) have played an important role in gait classification and the diagnosis of diseases (Lai et al., 2009). Studies have compared the predictive ability of LR and ANN (Dreiseitl and Ohno-Machado, 2002; Song et al., 2005). ANN modeling has been used in gait analysis focusing on pattern recognition (Hahn et al., 2005), as well as for classifying normal and pathological patterns (Lafuente et al., 1997; Su and Wu, 2000). SVM has recently been used for automated identification of gait pathologies (Begg et al., 2005; Lai et al., 2009). However, none of the past studies compared LR, probabilistic neural network (PNN) and SVM in classifying gait patterns or evaluated the effect of therapeutic interventions on ground reaction force (GRF) of PD patients. This study evaluated LR, PNN and SVM models for discriminating between normal and PD subjects using principal components derived from the GRF as input variables. For performance evaluation, the accuracies (ACC) and the areas under the receiver operating characteristic (ROC) curves (AUC) based on 1000 bootstrap runs of the classifiers were compared. The effects of DBS-STN on GRF with and without medication were also evaluated with both the models.
نتیجه گیری انگلیسی
The LR, PNN and SVM models presented high performance indexes for classifying GRF pattern of normal subjects and untreated PD. When using the bootstrap approach, PNN performed better according to AUC and NLR criteria and the SVM showed the best ACC. When evaluating the effect of treatments, the three classifiers indicated DBS-STN alone was more effective than medication alone, with the greatest improvement with combined treatments. However, PNN was more restrictive for accepting the patients’ GRF as normal.