Assessment of bradykinesia in Parkinson's disease patients through a multi-parametric system
The aim of this paper is to describe and present the results of the automatic detection and assessment of bradykinesia in motor disease patients using wireless, wearable accelerometers. The current work is related to a module of the PERFORM system, a FP7 project from the European Commission, that ai...
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| creator | Pastorino, M. Cancela, J. Arredondo, M. T. Pansera, M. Pastor-Sanz, L. Villagra, F. Pastor, M. A. Martin, J. A. |
| description | The aim of this paper is to describe and present the results of the automatic detection and assessment of bradykinesia in motor disease patients using wireless, wearable accelerometers. The current work is related to a module of the PERFORM system, a FP7 project from the European Commission, that aims at providing an innovative and reliable tool, able to evaluate, monitor and manage patients suffering from Parkinson's disease. The assessment procedure was carried out through a developed C# library that detects the activities of the patient using an activity recognition algorithm and classifies the data using a Support Vector Machine trained with data coming from previous test phases. The accuracy between the output of the automatic detection and the evaluation of the clinician both expressed with the Unified Parkinson's disease Rating Scale, presents an average value of [68.3±8.9]%. A meta-analysis algorithm is used in order to improve the accuracy to an average value of [74.4±14.9]%. Future work will include a personalized training of the classifiers in order to achieve a higher level of accuracy. |
| doi_str_mv | 10.1109/IEMBS.2011.6090516 |
| format | Conference Proceeding |
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T. ; Pansera, M. ; Pastor-Sanz, L. ; Villagra, F. ; Pastor, M. A. ; Martin, J. A.</creator><creatorcontrib>Pastorino, M. ; Cancela, J. ; Arredondo, M. T. ; Pansera, M. ; Pastor-Sanz, L. ; Villagra, F. ; Pastor, M. A. ; Martin, J. A.</creatorcontrib><description>The aim of this paper is to describe and present the results of the automatic detection and assessment of bradykinesia in motor disease patients using wireless, wearable accelerometers. The current work is related to a module of the PERFORM system, a FP7 project from the European Commission, that aims at providing an innovative and reliable tool, able to evaluate, monitor and manage patients suffering from Parkinson's disease. The assessment procedure was carried out through a developed C# library that detects the activities of the patient using an activity recognition algorithm and classifies the data using a Support Vector Machine trained with data coming from previous test phases. The accuracy between the output of the automatic detection and the evaluation of the clinician both expressed with the Unified Parkinson's disease Rating Scale, presents an average value of [68.3±8.9]%. A meta-analysis algorithm is used in order to improve the accuracy to an average value of [74.4±14.9]%. 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T.</creatorcontrib><creatorcontrib>Pansera, M.</creatorcontrib><creatorcontrib>Pastor-Sanz, L.</creatorcontrib><creatorcontrib>Villagra, F.</creatorcontrib><creatorcontrib>Pastor, M. A.</creatorcontrib><creatorcontrib>Martin, J. A.</creatorcontrib><title>Assessment of bradykinesia in Parkinson's disease patients through a multi-parametric system</title><title>2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society</title><addtitle>IEMBS</addtitle><addtitle>Conf Proc IEEE Eng Med Biol Soc</addtitle><description>The aim of this paper is to describe and present the results of the automatic detection and assessment of bradykinesia in motor disease patients using wireless, wearable accelerometers. The current work is related to a module of the PERFORM system, a FP7 project from the European Commission, that aims at providing an innovative and reliable tool, able to evaluate, monitor and manage patients suffering from Parkinson's disease. The assessment procedure was carried out through a developed C# library that detects the activities of the patient using an activity recognition algorithm and classifies the data using a Support Vector Machine trained with data coming from previous test phases. The accuracy between the output of the automatic detection and the evaluation of the clinician both expressed with the Unified Parkinson's disease Rating Scale, presents an average value of [68.3±8.9]%. A meta-analysis algorithm is used in order to improve the accuracy to an average value of [74.4±14.9]%. Future work will include a personalized training of the classifiers in order to achieve a higher level of accuracy.</description><subject>Accuracy</subject><subject>Actigraphy - instrumentation</subject><subject>Adult</subject><subject>Aged</subject><subject>Basal ganglia</subject><subject>Classification algorithms</subject><subject>Diagnosis, Computer-Assisted - instrumentation</subject><subject>Educational institutions</subject><subject>Equipment Design</subject><subject>Equipment Failure Analysis</subject><subject>Feature extraction</subject><subject>Female</subject><subject>Humans</subject><subject>Hypokinesia - diagnosis</subject><subject>Hypokinesia - etiology</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Monitoring, Ambulatory - instrumentation</subject><subject>Parkinson Disease - complications</subject><subject>Parkinson Disease - diagnosis</subject><subject>Parkinson's disease</subject><subject>Protocols</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>Support Vector Machine</subject><subject>Telemetry - instrumentation</subject><issn>1094-687X</issn><issn>1557-170X</issn><issn>1558-4615</issn><isbn>9781424441211</isbn><isbn>1424441218</isbn><isbn>1424441226</isbn><isbn>1457715899</isbn><isbn>9781457715891</isbn><isbn>9781424441228</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNo9kE1Lw0AQhtcvbK39AwqyN0-pO_uZPdZStVBRUMGDUHaTiV1t0pBND_33Bto6l2F4Hl54h5ArYCMAZu9m0-f7txFnACPNLFOgj8gFSC6lBM71MemDUmkiNagTMrQmPTCA044xKxOdms8eGcb4w7rR2grBz0mPc66kTlmffI1jxBhLrFq6LqhvXL79DRXG4Gio6Ktruiuuq9tI8xDRRaS1a0OnR9oum_Xme0kdLTerNiS1a1yJbRMyGrexxfKSnBVuFXG43wPy8TB9nzwl85fH2WQ8TzIhbZuYQhirlEttZovco3RgeY7ao2BpmnvPCwHceKVlbozJmMyYEZJxBcp6zsWA3Oxy640vMV_UTShds10canbC9U4IiPiP908VfxkkZIA</recordid><startdate>20110101</startdate><enddate>20110101</enddate><creator>Pastorino, M.</creator><creator>Cancela, J.</creator><creator>Arredondo, M. 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T.</au><au>Pansera, M.</au><au>Pastor-Sanz, L.</au><au>Villagra, F.</au><au>Pastor, M. A.</au><au>Martin, J. A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Assessment of bradykinesia in Parkinson's disease patients through a multi-parametric system</atitle><btitle>2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society</btitle><stitle>IEMBS</stitle><addtitle>Conf Proc IEEE Eng Med Biol Soc</addtitle><date>2011-01-01</date><risdate>2011</risdate><volume>2011</volume><spage>1810</spage><epage>1813</epage><pages>1810-1813</pages><issn>1094-687X</issn><issn>1557-170X</issn><eissn>1558-4615</eissn><isbn>9781424441211</isbn><isbn>1424441218</isbn><eisbn>1424441226</eisbn><eisbn>1457715899</eisbn><eisbn>9781457715891</eisbn><eisbn>9781424441228</eisbn><abstract>The aim of this paper is to describe and present the results of the automatic detection and assessment of bradykinesia in motor disease patients using wireless, wearable accelerometers. The current work is related to a module of the PERFORM system, a FP7 project from the European Commission, that aims at providing an innovative and reliable tool, able to evaluate, monitor and manage patients suffering from Parkinson's disease. The assessment procedure was carried out through a developed C# library that detects the activities of the patient using an activity recognition algorithm and classifies the data using a Support Vector Machine trained with data coming from previous test phases. The accuracy between the output of the automatic detection and the evaluation of the clinician both expressed with the Unified Parkinson's disease Rating Scale, presents an average value of [68.3±8.9]%. A meta-analysis algorithm is used in order to improve the accuracy to an average value of [74.4±14.9]%. Future work will include a personalized training of the classifiers in order to achieve a higher level of accuracy.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>22254680</pmid><doi>10.1109/IEMBS.2011.6090516</doi><tpages>4</tpages></addata></record> |
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| ispartof | 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011, Vol.2011, p.1810-1813 |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Accuracy Actigraphy - instrumentation Adult Aged Basal ganglia Classification algorithms Diagnosis, Computer-Assisted - instrumentation Educational institutions Equipment Design Equipment Failure Analysis Feature extraction Female Humans Hypokinesia - diagnosis Hypokinesia - etiology Male Middle Aged Monitoring, Ambulatory - instrumentation Parkinson Disease - complications Parkinson Disease - diagnosis Parkinson's disease Protocols Reproducibility of Results Sensitivity and Specificity Support Vector Machine Telemetry - instrumentation |
| title | Assessment of bradykinesia in Parkinson's disease patients through a multi-parametric system |
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