Loading…

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

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...

Full description

Saved in:
Bibliographic Details
Published in:2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2008-01, p.4700-4703
Main Authors: Muniz, A. M. S., Nobre, F. F., Liu, H., Lyons, K. E., Pahwa, R., Liu, W., Nadal, J.
Format: Article
Language:English
Subjects:
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 4703
container_issue
container_start_page 4700
container_title 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
container_volume
creator Muniz, A. M. S.
Nobre, F. F.
Liu, H.
Lyons, K. E.
Pahwa, R.
Liu, W.
Nadal, J.
description 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.
doi_str_mv 10.1109/IEMBS.2008.4650262
format article
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_4650262</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4650262</ieee_id><sourcerecordid>4650262</sourcerecordid><originalsourceid>FETCH-LOGICAL-i369t-bb305994138c2431a6b2dd231c8eeb48c482a2651619a75065894cb4eb50f97b3</originalsourceid><addsrcrecordid>eNpVkMtKAzEYheMNW2pfQDd5gan5c5tkWUvVQkVBBXclmf5DY-dSJinSt3fUbjybj8MHZ3EIuQY2AWD2djF_unudcMbMRGrFuOYnZGxzA5JLCQYUPyVDUMpkUoM6--ekOu8dszLTJv8YkHGMn6yPVEIbdkkGYEGLXKshaacxYow1Nom2JU0bpFiWWKT4U-Pep42rXB0KGlOo95VLoW1oaOiL67ahiX1Zh4guIt31rp-J1B-o61IoQxFcRRvcd79IX223vSIXpasijo8ckff7-dvsMVs-Pyxm02UWhLYp814wZa0EYQouBTjt-XrNBRQG0UtTSMMd1wo0WJcrppWxsvASvWKlzb0YkZu_3YCIq10XatcdVscnxTe24GJ3</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Assessment of the effects of subthalamic stimulation in Parkinson disease patients by artificial neural network</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Muniz, A. M. S. ; Nobre, F. F. ; Liu, H. ; Lyons, K. E. ; Pahwa, R. ; Liu, W. ; Nadal, J.</creator><creatorcontrib>Muniz, A. M. S. ; Nobre, F. F. ; Liu, H. ; Lyons, K. E. ; Pahwa, R. ; Liu, W. ; Nadal, J.</creatorcontrib><description>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.</description><identifier>ISSN: 1094-687X</identifier><identifier>ISBN: 9781424418145</identifier><identifier>ISBN: 1424418143</identifier><identifier>EISSN: 1558-4615</identifier><identifier>EISBN: 9781424418152</identifier><identifier>EISBN: 1424418151</identifier><identifier>DOI: 10.1109/IEMBS.2008.4650262</identifier><identifier>PMID: 19163765</identifier><language>eng</language><publisher>IEEE</publisher><subject>Artificial Neural Network ; Artificial neural networks ; Deep brain stimulation ; Diseases ; Force ; Gait analysis ; Legged locomotion ; Medical diagnostic imaging ; Parkinson Disease ; Satellite broadcasting</subject><ispartof>2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008-01, p.4700-4703</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4650262$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4650262$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Muniz, A. M. S.</creatorcontrib><creatorcontrib>Nobre, F. F.</creatorcontrib><creatorcontrib>Liu, H.</creatorcontrib><creatorcontrib>Lyons, K. E.</creatorcontrib><creatorcontrib>Pahwa, R.</creatorcontrib><creatorcontrib>Liu, W.</creatorcontrib><creatorcontrib>Nadal, J.</creatorcontrib><title>Assessment of the effects of subthalamic stimulation in Parkinson disease patients by artificial neural network</title><title>2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society</title><addtitle>IEMBS</addtitle><description>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.</description><subject>Artificial Neural Network</subject><subject>Artificial neural networks</subject><subject>Deep brain stimulation</subject><subject>Diseases</subject><subject>Force</subject><subject>Gait analysis</subject><subject>Legged locomotion</subject><subject>Medical diagnostic imaging</subject><subject>Parkinson Disease</subject><subject>Satellite broadcasting</subject><issn>1094-687X</issn><issn>1558-4615</issn><isbn>9781424418145</isbn><isbn>1424418143</isbn><isbn>9781424418152</isbn><isbn>1424418151</isbn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>6IE</sourceid><recordid>eNpVkMtKAzEYheMNW2pfQDd5gan5c5tkWUvVQkVBBXclmf5DY-dSJinSt3fUbjybj8MHZ3EIuQY2AWD2djF_unudcMbMRGrFuOYnZGxzA5JLCQYUPyVDUMpkUoM6--ekOu8dszLTJv8YkHGMn6yPVEIbdkkGYEGLXKshaacxYow1Nom2JU0bpFiWWKT4U-Pep42rXB0KGlOo95VLoW1oaOiL67ahiX1Zh4guIt31rp-J1B-o61IoQxFcRRvcd79IX223vSIXpasijo8ckff7-dvsMVs-Pyxm02UWhLYp814wZa0EYQouBTjt-XrNBRQG0UtTSMMd1wo0WJcrppWxsvASvWKlzb0YkZu_3YCIq10XatcdVscnxTe24GJ3</recordid><startdate>20080101</startdate><enddate>20080101</enddate><creator>Muniz, A. M. S.</creator><creator>Nobre, F. F.</creator><creator>Liu, H.</creator><creator>Lyons, K. E.</creator><creator>Pahwa, R.</creator><creator>Liu, W.</creator><creator>Nadal, J.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>20080101</creationdate><title>Assessment of the effects of subthalamic stimulation in Parkinson disease patients by artificial neural network</title><author>Muniz, A. M. S. ; Nobre, F. F. ; Liu, H. ; Lyons, K. E. ; Pahwa, R. ; Liu, W. ; Nadal, J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i369t-bb305994138c2431a6b2dd231c8eeb48c482a2651619a75065894cb4eb50f97b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Artificial Neural Network</topic><topic>Artificial neural networks</topic><topic>Deep brain stimulation</topic><topic>Diseases</topic><topic>Force</topic><topic>Gait analysis</topic><topic>Legged locomotion</topic><topic>Medical diagnostic imaging</topic><topic>Parkinson Disease</topic><topic>Satellite broadcasting</topic><toplevel>online_resources</toplevel><creatorcontrib>Muniz, A. M. S.</creatorcontrib><creatorcontrib>Nobre, F. F.</creatorcontrib><creatorcontrib>Liu, H.</creatorcontrib><creatorcontrib>Lyons, K. E.</creatorcontrib><creatorcontrib>Pahwa, R.</creatorcontrib><creatorcontrib>Liu, W.</creatorcontrib><creatorcontrib>Nadal, J.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection><jtitle>2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Muniz, A. M. S.</au><au>Nobre, F. F.</au><au>Liu, H.</au><au>Lyons, K. E.</au><au>Pahwa, R.</au><au>Liu, W.</au><au>Nadal, J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessment of the effects of subthalamic stimulation in Parkinson disease patients by artificial neural network</atitle><jtitle>2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society</jtitle><stitle>IEMBS</stitle><date>2008-01-01</date><risdate>2008</risdate><spage>4700</spage><epage>4703</epage><pages>4700-4703</pages><issn>1094-687X</issn><eissn>1558-4615</eissn><isbn>9781424418145</isbn><isbn>1424418143</isbn><eisbn>9781424418152</eisbn><eisbn>1424418151</eisbn><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.</abstract><pub>IEEE</pub><pmid>19163765</pmid><doi>10.1109/IEMBS.2008.4650262</doi><tpages>4</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 1094-687X
ispartof 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008-01, p.4700-4703
issn 1094-687X
1558-4615
language eng
recordid cdi_ieee_primary_4650262
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Artificial Neural Network
Artificial neural networks
Deep brain stimulation
Diseases
Force
Gait analysis
Legged locomotion
Medical diagnostic imaging
Parkinson Disease
Satellite broadcasting
title Assessment of the effects of subthalamic stimulation in Parkinson disease patients by artificial neural network
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-23T21%3A09%3A11IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Assessment%20of%20the%20effects%20of%20subthalamic%20stimulation%20in%20Parkinson%20disease%20patients%20by%20artificial%20neural%20network&rft.jtitle=2008%2030th%20Annual%20International%20Conference%20of%20the%20IEEE%20Engineering%20in%20Medicine%20and%20Biology%20Society&rft.au=Muniz,%20A.%20M.%20S.&rft.date=2008-01-01&rft.spage=4700&rft.epage=4703&rft.pages=4700-4703&rft.issn=1094-687X&rft.eissn=1558-4615&rft.isbn=9781424418145&rft.isbn_list=1424418143&rft_id=info:doi/10.1109/IEMBS.2008.4650262&rft.eisbn=9781424418152&rft.eisbn_list=1424418151&rft_dat=%3Cieee_6IE%3E4650262%3C/ieee_6IE%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i369t-bb305994138c2431a6b2dd231c8eeb48c482a2651619a75065894cb4eb50f97b3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/19163765&rft_ieee_id=4650262&rfr_iscdi=true