NDLI: Assessment of the effects of subthalamic stimulation in Parkinson disease patients by artificial neural network

Content Provider	IEEE Xplore Digital Library
Author	Muniz, A. M. S. Nobre, F. F. Liu, H. Lyons, K. E. Pahwa, R. Liu, W. Nadal, J.
Copyright Year	2008
Description	Author affiliation: Department of Physical Therapy and Rehabilitation Sciences, University of Kansas Medical Center, Kansas City, USA (Liu, H.; Liu, W.) \|\| Biomedical Engineering Program, COPPE, Federal University of Rio de Janeiro, Brazil P.O. Box 68.510, 21941-972 - BRAZIL (Muniz, A. M. S.; Nobre, F. F.; Nadal, J.) \|\| Parkinson's Disease and Movement Disorder Center, Department of Neurology, University of Kansas Medical Center, Kansas City, USA (Lyons, K. E.; Pahwa, R.)
Abstract	This study aims at applying an artificial neural network for the evaluation of the effects of deep brain stimulation (DBS) of the subthalamic nucleus (STN) on Parkinson disease (PD) patients with and without medication. A sample of 15 PD patients who have undergone STN DBS were evaluated under four test conditions: medication off and stimulation off (mof-sof), medication off and stimulation on (mof-son), medication on and stimulation off (mon-sof) and medication on and stimulation on (mon-son). A control group with 30 subjects was also evaluated. Principal component analysis (PCA) was applied on vertical ground reaction force (vGRF) and the first six principal component scores (PC score) were obtained in both groups. Those PCs scores were used as input in a probabilistic neural network (PNN). PNN presented satisfactory classification performance in the separation of controls and PD with 90.1% accuracy, 69.2% sensitivity and 100% specificity. The stimulation mof-son and mon-son conditions presented better results compared to mon-sof. In the mof-son condition, 41.7% were classified as normal, while further enhancement (63.3%) was given by the mon-son condition. These results indicated the potentiality of PNN to quantitatively evaluate treatment effects. Furthermore, STN DBS shows improvement on vGRF pattern in PD patients, most substantially when used with medication.
Starting Page	4700
Ending Page	4703
File Size	122583
Page Count	4
File Format	PDF
ISBN	9781424418145
ISSN	1557170X
DOI	10.1109/IEMBS.2008.4650262
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-20
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gait analysis Parkinson Disease Deep Brain Stimulation Artificial Neural Network
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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