A deep learning approach for Parkinson’s disease diagnosis from EEG signals
An automated detection system for Parkinson’s disease (PD) employing the convolutional neural network (CNN) is proposed in this study. PD is characterized by the gradual degradation of motor function in the brain. Since it is related to the brain abnormality, electroencephalogram (EEG) signals are u...
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| Veröffentlicht in: | Neural computing & applications 2020-08, Vol.32 (15), p.10927-10933 |
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| creator | Oh, Shu Lih Hagiwara, Yuki Raghavendra, U. Yuvaraj, Rajamanickam Arunkumar, N. Murugappan, M. Acharya, U. Rajendra |
| description | An automated detection system for Parkinson’s disease (PD) employing the convolutional neural network (CNN) is proposed in this study. PD is characterized by the gradual degradation of motor function in the brain. Since it is related to the brain abnormality, electroencephalogram (EEG) signals are usually considered for the early diagnosis. In this work, we have used the EEG signals of twenty PD and
twenty
normal subjects in this study. A
thirteen
-layer CNN architecture which can overcome the need for the conventional feature representation stages is implemented. The developed model has achieved a promising performance of 88.25% accuracy, 84.71% sensitivity, and 91.77% specificity. The developed classification model is ready to be used on large population before installation of clinical usage. |
| doi_str_mv | 10.1007/s00521-018-3689-5 |
| format | Article |
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twenty
normal subjects in this study. A
thirteen
-layer CNN architecture which can overcome the need for the conventional feature representation stages is implemented. The developed model has achieved a promising performance of 88.25% accuracy, 84.71% sensitivity, and 91.77% specificity. The developed classification model is ready to be used on large population before installation of clinical usage.</description><identifier>ISSN: 0941-0643</identifier><identifier>EISSN: 1433-3058</identifier><identifier>DOI: 10.1007/s00521-018-3689-5</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Artificial Intelligence ; Artificial neural networks ; Brain ; Computational Biology/Bioinformatics ; Computational Science and Engineering ; Computer Science ; Data Mining and Knowledge Discovery ; Deep learning ; Diagnosis ; Electroencephalography ; Image Processing and Computer Vision ; Parkinson's disease ; Probability and Statistics in Computer Science ; S.I. : Computer aided Medical Diagnosis</subject><ispartof>Neural computing & applications, 2020-08, Vol.32 (15), p.10927-10933</ispartof><rights>The Natural Computing Applications Forum 2018</rights><rights>The Natural Computing Applications Forum 2018.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c430t-9004d904db2839ca02c79049c62b28373c30223c953b7463fe8575d06ecbc2b23</citedby><cites>FETCH-LOGICAL-c430t-9004d904db2839ca02c79049c62b28373c30223c953b7463fe8575d06ecbc2b23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00521-018-3689-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00521-018-3689-5$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Oh, Shu Lih</creatorcontrib><creatorcontrib>Hagiwara, Yuki</creatorcontrib><creatorcontrib>Raghavendra, U.</creatorcontrib><creatorcontrib>Yuvaraj, Rajamanickam</creatorcontrib><creatorcontrib>Arunkumar, N.</creatorcontrib><creatorcontrib>Murugappan, M.</creatorcontrib><creatorcontrib>Acharya, U. Rajendra</creatorcontrib><title>A deep learning approach for Parkinson’s disease diagnosis from EEG signals</title><title>Neural computing & applications</title><addtitle>Neural Comput & Applic</addtitle><description>An automated detection system for Parkinson’s disease (PD) employing the convolutional neural network (CNN) is proposed in this study. PD is characterized by the gradual degradation of motor function in the brain. Since it is related to the brain abnormality, electroencephalogram (EEG) signals are usually considered for the early diagnosis. In this work, we have used the EEG signals of twenty PD and
twenty
normal subjects in this study. A
thirteen
-layer CNN architecture which can overcome the need for the conventional feature representation stages is implemented. The developed model has achieved a promising performance of 88.25% accuracy, 84.71% sensitivity, and 91.77% specificity. The developed classification model is ready to be used on large population before installation of clinical usage.</description><subject>Artificial Intelligence</subject><subject>Artificial neural networks</subject><subject>Brain</subject><subject>Computational Biology/Bioinformatics</subject><subject>Computational Science and Engineering</subject><subject>Computer Science</subject><subject>Data Mining and Knowledge Discovery</subject><subject>Deep learning</subject><subject>Diagnosis</subject><subject>Electroencephalography</subject><subject>Image Processing and Computer Vision</subject><subject>Parkinson's disease</subject><subject>Probability and Statistics in Computer Science</subject><subject>S.I. : Computer aided Medical Diagnosis</subject><issn>0941-0643</issn><issn>1433-3058</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp1UE1PwzAMjRBIjMEP4BaJc8H5bHucpjGQhuAA5yhL09KxpSXeDtz4G_w9fgmpisQJydaT7fds6xFyyeCaAeQ3CKA4y4AVmdBFmakjMmFSiEyAKo7JBEqZplqKU3KGuAEAqQs1IQ8zWnnf0623MbShobbvY2fdK627SJ9sfGsDduH78wtp1aK36BPaJnTYIq1jt6OLxZJi2wS7xXNyUifwF784JS-3i-f5XbZ6XN7PZ6vMSQH7rEzXqzLlmheidBa4y1NZOs2HTi6cAM6FK5VY51KL2hcqVxVo79YuUcSUXI1706_vB497s-kOcfjAcMml0kMkFhtZLnaI0demj-3Oxg_DwAyumdE1k1wzg2tGJQ0fNZi4ofHxb_P_oh_1DG6a</recordid><startdate>20200801</startdate><enddate>20200801</enddate><creator>Oh, Shu Lih</creator><creator>Hagiwara, Yuki</creator><creator>Raghavendra, U.</creator><creator>Yuvaraj, Rajamanickam</creator><creator>Arunkumar, N.</creator><creator>Murugappan, M.</creator><creator>Acharya, U. 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Rajendra</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c430t-9004d904db2839ca02c79049c62b28373c30223c953b7463fe8575d06ecbc2b23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Artificial Intelligence</topic><topic>Artificial neural networks</topic><topic>Brain</topic><topic>Computational Biology/Bioinformatics</topic><topic>Computational Science and Engineering</topic><topic>Computer Science</topic><topic>Data Mining and Knowledge Discovery</topic><topic>Deep learning</topic><topic>Diagnosis</topic><topic>Electroencephalography</topic><topic>Image Processing and Computer Vision</topic><topic>Parkinson's disease</topic><topic>Probability and Statistics in Computer Science</topic><topic>S.I. : Computer aided Medical Diagnosis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Oh, Shu Lih</creatorcontrib><creatorcontrib>Hagiwara, Yuki</creatorcontrib><creatorcontrib>Raghavendra, U.</creatorcontrib><creatorcontrib>Yuvaraj, Rajamanickam</creatorcontrib><creatorcontrib>Arunkumar, N.</creatorcontrib><creatorcontrib>Murugappan, M.</creatorcontrib><creatorcontrib>Acharya, U. 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Rajendra</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A deep learning approach for Parkinson’s disease diagnosis from EEG signals</atitle><jtitle>Neural computing & applications</jtitle><stitle>Neural Comput & Applic</stitle><date>2020-08-01</date><risdate>2020</risdate><volume>32</volume><issue>15</issue><spage>10927</spage><epage>10933</epage><pages>10927-10933</pages><issn>0941-0643</issn><eissn>1433-3058</eissn><abstract>An automated detection system for Parkinson’s disease (PD) employing the convolutional neural network (CNN) is proposed in this study. PD is characterized by the gradual degradation of motor function in the brain. Since it is related to the brain abnormality, electroencephalogram (EEG) signals are usually considered for the early diagnosis. In this work, we have used the EEG signals of twenty PD and
twenty
normal subjects in this study. A
thirteen
-layer CNN architecture which can overcome the need for the conventional feature representation stages is implemented. The developed model has achieved a promising performance of 88.25% accuracy, 84.71% sensitivity, and 91.77% specificity. The developed classification model is ready to be used on large population before installation of clinical usage.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s00521-018-3689-5</doi><tpages>7</tpages></addata></record> |
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| source | SpringerNature Journals |
| subjects | Artificial Intelligence Artificial neural networks Brain Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Deep learning Diagnosis Electroencephalography Image Processing and Computer Vision Parkinson's disease Probability and Statistics in Computer Science S.I. : Computer aided Medical Diagnosis |
| title | A deep learning approach for Parkinson’s disease diagnosis from EEG signals |
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