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...
Uloženo v:
| Vydáno v: | Neural computing & applications Ročník 32; číslo 15; s. 10927 - 10933 |
|---|---|
| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
London
Springer London
01.08.2020
Springer Nature B.V |
| Témata: | |
| ISSN: | 0941-0643, 1433-3058 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| 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. |
|---|---|
| AbstractList | 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. 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. |
| Author | Oh, Shu Lih Hagiwara, Yuki Yuvaraj, Rajamanickam Raghavendra, U. Acharya, U. Rajendra Arunkumar, N. Murugappan, M. |
| Author_xml | – sequence: 1 givenname: Shu Lih surname: Oh fullname: Oh, Shu Lih organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic – sequence: 2 givenname: Yuki surname: Hagiwara fullname: Hagiwara, Yuki organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic – sequence: 3 givenname: U. surname: Raghavendra fullname: Raghavendra, U. organization: Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education – sequence: 4 givenname: Rajamanickam surname: Yuvaraj fullname: Yuvaraj, Rajamanickam organization: School of Electrical and Electronic Engineering, Nanyang Technological University – sequence: 5 givenname: N. surname: Arunkumar fullname: Arunkumar, N. organization: Department of Electronics and Instrumentation, SASTRA University – sequence: 6 givenname: M. surname: Murugappan fullname: Murugappan, M. organization: Kuwait College of Science and Technology – sequence: 7 givenname: U. Rajendra surname: Acharya fullname: Acharya, U. Rajendra email: aru@np.edu.sg organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, School of Medicine, Faculty of Health and Medical Sciences, Taylor’s University |
| BookMark | eNp9kM1KAzEUhYNUsFUfwF3A9Wh-JzPLUmoVKrrQdcjMJGNqm4zJdOHO1_D1fBIzjCAIFi4cLjlf7uHMwMR5pwG4wOgKIySuI0Kc4AzhIqN5UWb8CEwxozSjiBcTMEUlS685oydgFuMGIcTygk_B_Rw2Wndwq1Vw1rVQdV3wqn6Bxgf4qMKrddG7r4_PCBsbtYo6qWqdjzZCE_wOLpcrGG3r1DaegWOTRJ__6Cl4vlk-LW6z9cPqbjFfZzXDqM9wXjEqOFekqmhaaMOFUAVRrMSVyQXJkeANS2OUNoYa0pQmLZhzpBXP6Sm4HP9NWd_2OvZy4_dhSCApwQVnggh8yEUYYTwfJrnw6KqDjzFoI7tgdyq8S4zk0K0cu5WpWzl0K3lixB-mtr3qrXd9UHZ7kCQjGdMV1-rwm-l_6BvOXY6- |
| CitedBy_id | crossref_primary_10_1016_j_neuroimage_2024_120750 crossref_primary_10_3390_fractalfract5040225 crossref_primary_10_1007_s11571_023_10016_6 crossref_primary_10_1109_LSENS_2025_3565312 crossref_primary_10_1155_2022_7603319 crossref_primary_10_1007_s41666_023_00130_9 crossref_primary_10_1007_s11517_025_03388_w crossref_primary_10_1371_journal_pone_0274263 crossref_primary_10_3390_app14093883 crossref_primary_10_3390_electronics10141740 crossref_primary_10_1038_s41598_022_06497_w crossref_primary_10_2174_1574893618666230706112826 crossref_primary_10_1016_j_nexres_2025_100473 crossref_primary_10_1371_journal_pone_0315453 crossref_primary_10_1016_j_jestch_2024_101855 crossref_primary_10_1186_s40708_025_00260_3 crossref_primary_10_1002_tee_23646 crossref_primary_10_1016_j_engappai_2022_105540 crossref_primary_10_1007_s13198_024_02591_6 crossref_primary_10_1016_j_bspc_2022_104281 crossref_primary_10_3390_electronics12040783 crossref_primary_10_1016_j_measurement_2018_11_045 crossref_primary_10_1038_s41598_021_93543_8 crossref_primary_10_1016_j_bspc_2023_105872 crossref_primary_10_1016_j_compbiomed_2022_106469 crossref_primary_10_1007_s11042_019_7483_x crossref_primary_10_1109_JBHI_2022_3232811 crossref_primary_10_1080_13682199_2022_2146885 crossref_primary_10_1016_j_bbe_2022_07_007 crossref_primary_10_1007_s12031_025_02329_4 crossref_primary_10_1016_j_biomaterials_2025_123548 crossref_primary_10_1016_j_compbiolchem_2024_108228 crossref_primary_10_1007_s11042_022_12960_7 crossref_primary_10_1007_s11042_019_7555_y crossref_primary_10_1016_j_artmed_2019_07_006 crossref_primary_10_1007_s11517_021_02354_6 crossref_primary_10_3390_s22197154 crossref_primary_10_3390_s21093050 crossref_primary_10_1007_s40843_022_2165_8 crossref_primary_10_1109_ACCESS_2023_3277756 crossref_primary_10_1016_j_bbe_2022_04_003 crossref_primary_10_3390_math11020376 crossref_primary_10_3390_electronics11142265 crossref_primary_10_1007_s13721_025_00583_5 crossref_primary_10_1007_s40747_021_00408_8 crossref_primary_10_1016_j_nicl_2022_103266 crossref_primary_10_1016_j_measurement_2018_11_075 crossref_primary_10_13005_bpj_3076 crossref_primary_10_1007_s41782_024_00292_5 crossref_primary_10_1371_journal_pone_0277555 crossref_primary_10_1080_1206212X_2018_1553272 crossref_primary_10_3390_diagnostics12081796 crossref_primary_10_1016_j_inffus_2022_12_019 crossref_primary_10_1016_j_cose_2021_102220 crossref_primary_10_1007_s11042_024_18398_3 crossref_primary_10_1111_exsy_13569 crossref_primary_10_1002_tee_70094 crossref_primary_10_3389_fnbot_2022_953968 crossref_primary_10_3390_app10238662 crossref_primary_10_3390_diagnostics11061076 crossref_primary_10_1177_09727531251351067 crossref_primary_10_1007_s00521_022_07809_x crossref_primary_10_1109_ACCESS_2021_3089358 crossref_primary_10_1155_2022_7242667 crossref_primary_10_3390_app12146967 crossref_primary_10_3390_s23218936 crossref_primary_10_1080_02648725_2023_2200333 crossref_primary_10_1109_TNSRE_2023_3324343 crossref_primary_10_1016_j_bspc_2022_103774 crossref_primary_10_1111_exsy_12472 crossref_primary_10_3390_healthcare9060740 crossref_primary_10_3390_sym15020535 crossref_primary_10_1016_j_cmpb_2023_107496 crossref_primary_10_1016_j_cmpb_2019_105205 crossref_primary_10_1016_j_measurement_2018_11_064 crossref_primary_10_1108_IJCHM_09_2021_1176 crossref_primary_10_1007_s41870_024_02377_w crossref_primary_10_1088_1741_2552_ade6a9 crossref_primary_10_1109_TNSRE_2023_3323902 crossref_primary_10_3390_electronics12132856 crossref_primary_10_1016_j_compbiomed_2024_108462 crossref_primary_10_1159_000547180 crossref_primary_10_1007_s42979_025_03685_z crossref_primary_10_1007_s11042_019_7533_4 crossref_primary_10_1016_j_eswa_2024_125713 crossref_primary_10_1088_1741_2552_acc612 crossref_primary_10_3389_fphys_2023_1227952 crossref_primary_10_1109_TCAD_2024_3388908 crossref_primary_10_1007_s13369_021_06544_0 crossref_primary_10_1039_D4RA05560H crossref_primary_10_1007_s11042_019_7474_y crossref_primary_10_1007_s00521_024_09569_2 crossref_primary_10_1016_j_bj_2024_100803 crossref_primary_10_1109_JSEN_2021_3080135 crossref_primary_10_1007_s13369_022_07249_8 crossref_primary_10_7717_peerj_cs_3135 crossref_primary_10_1016_j_eswa_2023_120541 crossref_primary_10_3390_info11120556 crossref_primary_10_3390_a15010005 crossref_primary_10_4108_eetpht_10_5568 crossref_primary_10_1186_s12883_023_03468_0 crossref_primary_10_3389_fncom_2022_1006763 crossref_primary_10_1007_s11042_019_07765_0 crossref_primary_10_1007_s00521_021_06626_y crossref_primary_10_1016_j_bspc_2022_104116 crossref_primary_10_1109_ACCESS_2024_3421302 crossref_primary_10_3390_diagnostics14192181 crossref_primary_10_3390_s23052458 crossref_primary_10_1007_s11042_019_7191_6 crossref_primary_10_1109_ACCESS_2023_3319248 crossref_primary_10_1080_21681163_2024_2304574 crossref_primary_10_1016_j_arr_2024_102285 crossref_primary_10_1109_TCDS_2024_3386364 crossref_primary_10_1038_s41746_021_00536_y crossref_primary_10_4103_jehp_jehp_1777_23 crossref_primary_10_1007_s00521_020_05505_2 crossref_primary_10_3389_fnins_2021_741651 crossref_primary_10_1016_j_bspc_2025_108057 crossref_primary_10_1002_ima_23120 crossref_primary_10_1007_s00521_018_3889_z crossref_primary_10_1109_TETC_2021_3054761 crossref_primary_10_1007_s11042_019_7532_5 crossref_primary_10_1515_bams_2021_0064 crossref_primary_10_3390_bioengineering10010066 crossref_primary_10_1111_exsy_12787 crossref_primary_10_1080_1206212X_2018_1554836 crossref_primary_10_1007_s13198_025_02966_3 crossref_primary_10_1109_JBHI_2023_3292452 crossref_primary_10_1007_s12539_024_00634_x crossref_primary_10_1080_10255842_2023_2271603 crossref_primary_10_3390_computers12030058 crossref_primary_10_1016_j_compbiomed_2024_107959 crossref_primary_10_3389_fnins_2024_1433583 crossref_primary_10_1007_s12144_025_07337_6 crossref_primary_10_1109_JIOT_2022_3232793 crossref_primary_10_3390_computers11030047 crossref_primary_10_3390_s22187065 crossref_primary_10_1109_JSEN_2025_3562448 crossref_primary_10_1109_JTEHM_2021_3050925 crossref_primary_10_3390_app14219747 crossref_primary_10_1007_s11571_022_09904_0 crossref_primary_10_1080_21681163_2022_2111721 crossref_primary_10_1109_ACCESS_2019_2948430 crossref_primary_10_3390_a15120474 crossref_primary_10_1109_ACCESS_2023_3275024 crossref_primary_10_1007_s10278_024_01316_2 crossref_primary_10_1088_1741_2552_ac40a0 crossref_primary_10_1016_j_parkreldis_2020_08_001 crossref_primary_10_1109_TCBB_2021_3052811 crossref_primary_10_1186_s41983_024_00839_3 crossref_primary_10_1088_1741_2552_ad9957 crossref_primary_10_1109_ACCESS_2024_3487001 crossref_primary_10_1007_s10916_019_1519_7 crossref_primary_10_3390_app9142789 crossref_primary_10_3390_app13042698 crossref_primary_10_3390_jpm11010038 crossref_primary_10_1371_journal_pone_0273383 crossref_primary_10_1016_j_ajp_2025_104537 crossref_primary_10_1007_s11042_024_20466_7 crossref_primary_10_1007_s12264_021_00749_x crossref_primary_10_3389_fnagi_2025_1586282 crossref_primary_10_1038_s41746_023_00983_9 crossref_primary_10_1109_TNNLS_2022_3220551 crossref_primary_10_1007_s00521_020_05467_5 crossref_primary_10_1016_j_procs_2024_11_165 crossref_primary_10_1016_j_engappai_2023_106882 crossref_primary_10_1038_s41598_022_23247_0 crossref_primary_10_3390_s20102860 crossref_primary_10_1007_s40747_020_00257_x crossref_primary_10_1109_ACCESS_2025_3575023 crossref_primary_10_1016_j_jneumeth_2021_109282 crossref_primary_10_32604_cmc_2022_023124 crossref_primary_10_3390_s21196343 crossref_primary_10_1007_s00521_024_09521_4 crossref_primary_10_1177_0954411920966937 crossref_primary_10_1051_bioconf_20236101014 crossref_primary_10_1109_TAI_2022_3193651 crossref_primary_10_1007_s13246_023_01284_x crossref_primary_10_1007_s11042_024_18186_z crossref_primary_10_1109_ACCESS_2025_3593254 crossref_primary_10_34248_bsengineering_858918 crossref_primary_10_1080_00207454_2020_1733559 crossref_primary_10_1088_2632_2153_ad370d crossref_primary_10_1007_s13534_023_00319_2 crossref_primary_10_1371_journal_pone_0286506 crossref_primary_10_3390_math11214409 crossref_primary_10_3390_s23208609 crossref_primary_10_1186_s12938_024_01250_y crossref_primary_10_1155_2022_3590973 crossref_primary_10_1007_s10462_021_10062_8 crossref_primary_10_1177_20552076241297355 crossref_primary_10_1007_s11831_022_09710_1 crossref_primary_10_3390_ijerph16040599 crossref_primary_10_4018_IJSI_292027 crossref_primary_10_1007_s00530_020_00694_1 crossref_primary_10_1109_ACCESS_2025_3583572 crossref_primary_10_1007_s11760_019_01564_8 crossref_primary_10_1109_TIM_2024_3351248 crossref_primary_10_1002_adfm_202101099 crossref_primary_10_1007_s11571_022_09817_y crossref_primary_10_1088_2632_072X_ac5f8d crossref_primary_10_1109_TDSC_2021_3124337 crossref_primary_10_1007_s11042_019_07790_z crossref_primary_10_1007_s11042_019_7534_3 crossref_primary_10_1016_j_bspc_2023_105467 crossref_primary_10_1016_j_heliyon_2024_e25469 crossref_primary_10_1016_j_neunet_2021_11_025 crossref_primary_10_3390_app9142870 crossref_primary_10_3390_app142210511 crossref_primary_10_1080_1206212X_2018_1550166 crossref_primary_10_1016_j_bspc_2024_106946 crossref_primary_10_56294_sctconf20251353 crossref_primary_10_1016_j_compbiomed_2025_110608 crossref_primary_10_1016_j_patrec_2020_03_009 crossref_primary_10_4103_JNMO_JNMO_10_23 crossref_primary_10_26634_jse_15_3_18133 crossref_primary_10_1016_j_compmedimag_2024_102400 crossref_primary_10_1109_ACCESS_2023_3325896 crossref_primary_10_1109_TCBB_2023_3246961 crossref_primary_10_1155_2021_5511922 crossref_primary_10_1155_2021_5531754 crossref_primary_10_3389_fneur_2021_638266 crossref_primary_10_1007_s13369_019_04197_8 crossref_primary_10_1016_j_bspc_2021_102777 crossref_primary_10_1016_j_bbe_2021_11_003 crossref_primary_10_1109_ACCESS_2021_3133326 crossref_primary_10_3390_computers13110293 crossref_primary_10_1007_s00521_022_07509_6 crossref_primary_10_1007_s11042_024_20276_x crossref_primary_10_1016_j_imu_2023_101352 crossref_primary_10_1007_s42600_022_00215_1 crossref_primary_10_1007_s11042_019_7537_0 crossref_primary_10_1016_j_bspc_2023_104870 crossref_primary_10_1109_ACCESS_2020_3044732 crossref_primary_10_1631_jzus_B2400103 crossref_primary_10_1155_2022_9276579 crossref_primary_10_1186_s12984_020_00684_4 crossref_primary_10_1016_j_neuroscience_2024_11_030 crossref_primary_10_1109_LSENS_2021_3074958 crossref_primary_10_1007_s10462_025_11347_y crossref_primary_10_1007_s11571_022_09886_z crossref_primary_10_1371_journal_pone_0263159 crossref_primary_10_1007_s11042_019_7161_z crossref_primary_10_1109_ACCESS_2025_3533703 crossref_primary_10_1016_j_nbd_2025_106915 crossref_primary_10_1007_s11571_020_09655_w crossref_primary_10_1016_j_eswa_2022_117362 crossref_primary_10_1515_bmt_2022_0022 crossref_primary_10_1109_TNNLS_2020_3016666 crossref_primary_10_1007_s10489_023_05155_6 crossref_primary_10_1162_netn_a_00353 crossref_primary_10_1109_ACCESS_2019_2899188 crossref_primary_10_1109_ACCESS_2018_2886708 crossref_primary_10_3389_fmed_2024_1453743 crossref_primary_10_3390_diagnostics12051033 crossref_primary_10_32604_jnm_2022_027040 crossref_primary_10_3390_s22052028 crossref_primary_10_1007_s11042_019_07759_y crossref_primary_10_1016_j_bbe_2021_04_008 crossref_primary_10_3390_diagnostics13061088 crossref_primary_10_1186_s13064_025_00212_x crossref_primary_10_1016_j_cmpb_2023_107683 crossref_primary_10_1177_1550059420916634 crossref_primary_10_15857_ksep_2021_30_2_131 crossref_primary_10_1177_20552076221075147 crossref_primary_10_3390_s24134273 crossref_primary_10_1016_j_molliq_2023_122479 crossref_primary_10_1111_exsy_12957 crossref_primary_10_3390_bioengineering11100986 crossref_primary_10_1109_ACCESS_2022_3197645 crossref_primary_10_1007_s11042_022_13947_0 crossref_primary_10_3390_math11194065 crossref_primary_10_1016_j_drudis_2024_104216 crossref_primary_10_3390_bioengineering9030116 crossref_primary_10_3390_s21217034 crossref_primary_10_1007_s11042_019_7173_8 crossref_primary_10_1016_j_compbiomed_2023_107031 crossref_primary_10_1109_TKDE_2021_3128770 crossref_primary_10_1016_j_ssci_2019_04_007 crossref_primary_10_1007_s10462_021_10084_2 crossref_primary_10_3389_fnins_2024_1373515 |
| Cites_doi | 10.1016/j.bspc.2012.08.004 10.1007/978-3-642-15825-4_10 10.1038/nrd3115 10.1016/j.ijpsycho.2014.07.014 10.1016/j.cmpb.2018.04.012 10.1016/j.ins.2017.04.012 10.1038/14819 10.1016/j.jocs.2017.02.006 10.1101/cshperspect.a020602 10.1016/j.future.2017.08.039 10.1016/S0079-6123(10)83004-3 10.1016/j.bspc.2012.04.007 10.1007/s11065-010-9148-4 10.1016/j.knosys.2017.06.003 10.1016/j.cmpb.2018.04.005 10.1212/WNL.17.5.427 10.1016/j.bspc.2013.02.006 10.1109/IJCNN.2011.6033458 10.1142/S0129065710002589 10.1016/j.eswa.2009.01.022 10.1016/j.neuropharm.2011.02.007 10.1016/j.compbiomed.2017.09.017 10.1016/j.psychres.2009.04.004 10.1109/CVPR.2005.254 10.1016/j.eswa.2012.07.014 10.1155/2014/985789 10.1007/s11571-013-9247-z 10.1080/03091900701559408 |
| ContentType | Journal Article |
| Copyright | The Natural Computing Applications Forum 2018 The Natural Computing Applications Forum 2018. Copyright Springer Nature B.V. Aug 2020 |
| Copyright_xml | – notice: The Natural Computing Applications Forum 2018 – notice: The Natural Computing Applications Forum 2018. – notice: Copyright Springer Nature B.V. Aug 2020 |
| DBID | AAYXX CITATION 8FE 8FG AFKRA ARAPS BENPR BGLVJ CCPQU DWQXO HCIFZ P5Z P62 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI PRINS |
| DOI | 10.1007/s00521-018-3689-5 |
| DatabaseName | CrossRef ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Technology collection ProQuest One Community College ProQuest Central SciTech Premium Collection Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China |
| DatabaseTitle | CrossRef Advanced Technologies & Aerospace Collection Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest One Academic Eastern Edition SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central Advanced Technologies & Aerospace Database ProQuest One Applied & Life Sciences ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) |
| DatabaseTitleList | Advanced Technologies & Aerospace Collection Advanced Technologies & Aerospace Collection |
| Database_xml | – sequence: 1 dbid: P5Z name: Advanced Technologies & Aerospace Database url: https://search.proquest.com/hightechjournals sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science Engineering |
| EISSN | 1433-3058 |
| EndPage | 10933 |
| ExternalDocumentID | 10_1007_s00521_018_3689_5 |
| GroupedDBID | -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 123 1N0 1SB 2.D 203 28- 29N 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 53G 5QI 5VS 67Z 6NX 8FE 8FG 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AAPKM AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBRH ABBXA ABDBE ABDBF ABDZT ABECU ABFSG ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABLJU ABMNI ABMQK ABNWP ABQBU ABQSL ABRTQ ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACSTC ACUHS ACZOJ ADHHG ADHIR ADHKG ADIMF ADKFA ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AEZWR AFBBN AFDZB AFEXP AFGCZ AFHIU AFKRA AFLOW AFOHR AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGQPQ AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHPBZ AHSBF AHWEU AHYZX AIAKS AIGIU AIIXL AILAN AITGF AIXLP AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG ATHPR AVWKF AXYYD AYFIA AYJHY AZFZN B-. B0M BA0 BBWZM BDATZ BENPR BGLVJ BGNMA BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EAD EAP EBLON EBS ECS EDO EIOEI EJD EMI EMK EPL ESBYG EST ESX F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW LAS LLZTM M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P2P P62 P9O PF0 PHGZM PHGZT PQGLB PT4 PT5 QOK QOS R4E R89 R9I RHV RIG RNI RNS ROL RPX RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 ZMTXR ~8M ~EX AAYXX AFFHD CITATION DWQXO PKEHL PQEST PQQKQ PQUKI PRINS |
| ID | FETCH-LOGICAL-c410t-16b43755a2bb316b3d577a82a491bf6726075d45d4faeff3f2d9fd4f1550ea563 |
| IEDL.DBID | RSV |
| ISICitedReferencesCount | 414 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000549646700010&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0941-0643 |
| IngestDate | Wed Nov 05 08:25:27 EST 2025 Wed Nov 05 02:18:11 EST 2025 Sat Nov 29 07:46:43 EST 2025 Tue Nov 18 22:41:04 EST 2025 Mon Jul 21 06:07:21 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 15 |
| Keywords | Deep learning Computer-aided detection system Convolutional neural network Parkinson’s disease |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c410t-16b43755a2bb316b3d577a82a491bf6726075d45d4faeff3f2d9fd4f1550ea563 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 2424564564 |
| PQPubID | 2043988 |
| PageCount | 7 |
| ParticipantIDs | proquest_journals_3218547271 proquest_journals_2424564564 crossref_primary_10_1007_s00521_018_3689_5 crossref_citationtrail_10_1007_s00521_018_3689_5 springer_journals_10_1007_s00521_018_3689_5 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-08-01 |
| PublicationDateYYYYMMDD | 2020-08-01 |
| PublicationDate_xml | – month: 08 year: 2020 text: 2020-08-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: Heidelberg |
| PublicationTitle | Neural computing & applications |
| PublicationTitleAbbrev | Neural Comput & Applic |
| PublicationYear | 2020 |
| Publisher | Springer London Springer Nature B.V |
| Publisher_xml | – name: Springer London – name: Springer Nature B.V |
| References | 3689_CR5 HL Chen (3689_CR31) 2013; 40 3689_CR4 AF Leuchter (3689_CR9) 2009; 169 UR Acharya (3689_CR18) 2017; 132 UR Acharya (3689_CR23) 2018; 79 CAM Lima (3689_CR8) 2009; 36 UR Acharya (3689_CR17) 2017; 405 UR Acharya (3689_CR20) 2018; 13 C Ma (3689_CR33) 2014; 2014 R Yuvaraj (3689_CR7) 2016; 28 H Hampal (3689_CR11) 2010; 9 RJ Martis (3689_CR14) 2013; 8 UR Acharya (3689_CR22) 2018; 161 O Faust (3689_CR24) 2018; 161 UR Acharya (3689_CR13) 2010; 20 J Stiles (3689_CR1) 2010; 20 UR Acharya (3689_CR21) 2017 M Yahr (3689_CR30) 1967; 17 DJ Surmeier (3689_CR3) 2010; 183 3689_CR29 3689_CR28 CX Han (3689_CR6) 2013; 7 MJ Gandal (3689_CR10) 2012; 62 WL Zuo (3689_CR32) 2013; 8 3689_CR27 M Riesenhuber (3689_CR25) 1999; 2 3689_CR26 MR Daliri (3689_CR34) 2013; 8 J Silver (3689_CR2) 2015; 7 KC Chua (3689_CR12) 2009; 33 JH Tan (3689_CR19) 2017; 20 A Krizhevsky (3689_CR16) 2012; 1 R Yuvraj (3689_CR15) 2014; 94 |
| References_xml | – volume: 8 start-page: 193 year: 2013 ident: 3689_CR14 publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2012.08.004 – ident: 3689_CR27 doi: 10.1007/978-3-642-15825-4_10 – volume: 9 start-page: 560 issue: 7 year: 2010 ident: 3689_CR11 publication-title: Nat Rev Drug Discov doi: 10.1038/nrd3115 – volume: 94 start-page: 482 issue: 3 year: 2014 ident: 3689_CR15 publication-title: Int J Psychophysiol doi: 10.1016/j.ijpsycho.2014.07.014 – volume: 1 start-page: 1097 year: 2012 ident: 3689_CR16 publication-title: Adv Neural Inf Process Syst – volume: 161 start-page: 103 year: 2018 ident: 3689_CR22 publication-title: Comput Methods Prog Biomed doi: 10.1016/j.cmpb.2018.04.012 – volume: 405 start-page: 81 year: 2017 ident: 3689_CR17 publication-title: Inf Sci doi: 10.1016/j.ins.2017.04.012 – volume: 2 start-page: 1019 year: 1999 ident: 3689_CR25 publication-title: Nat Neurosci doi: 10.1038/14819 – volume: 20 start-page: 70 year: 2017 ident: 3689_CR19 publication-title: J Comput Sci doi: 10.1016/j.jocs.2017.02.006 – volume: 7 start-page: a020602 issue: 3 year: 2015 ident: 3689_CR2 publication-title: Cold Spring Harb Perspect Biol doi: 10.1101/cshperspect.a020602 – volume: 79 start-page: 952 issue: 3 year: 2018 ident: 3689_CR23 publication-title: Future Gener Comput Syst doi: 10.1016/j.future.2017.08.039 – volume: 183 start-page: 59 year: 2010 ident: 3689_CR3 publication-title: Prog Brain Res doi: 10.1016/S0079-6123(10)83004-3 – volume: 8 start-page: 66 issue: 1 year: 2013 ident: 3689_CR34 publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2012.04.007 – volume: 20 start-page: 327 issue: 4 year: 2010 ident: 3689_CR1 publication-title: Neuropsychol Rev doi: 10.1007/s11065-010-9148-4 – ident: 3689_CR5 – volume: 132 start-page: 62 year: 2017 ident: 3689_CR18 publication-title: Knowl Based Syst doi: 10.1016/j.knosys.2017.06.003 – volume: 161 start-page: 1 year: 2018 ident: 3689_CR24 publication-title: Comput Methods Prog Biomed doi: 10.1016/j.cmpb.2018.04.005 – volume: 17 start-page: 427 issue: 5 year: 1967 ident: 3689_CR30 publication-title: Neurology doi: 10.1212/WNL.17.5.427 – volume: 8 start-page: 364 issue: 4 year: 2013 ident: 3689_CR32 publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2013.02.006 – volume: 28 start-page: 12 year: 2016 ident: 3689_CR7 publication-title: Neural Comput Appl – ident: 3689_CR26 doi: 10.1109/IJCNN.2011.6033458 – volume: 20 start-page: 509 issue: 6 year: 2010 ident: 3689_CR13 publication-title: Int J Neutral Syst doi: 10.1142/S0129065710002589 – volume: 36 start-page: 10054 issue: 6 year: 2009 ident: 3689_CR8 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2009.01.022 – volume: 62 start-page: 1504 issue: 3 year: 2012 ident: 3689_CR10 publication-title: Neuropharmacology doi: 10.1016/j.neuropharm.2011.02.007 – ident: 3689_CR29 – year: 2017 ident: 3689_CR21 publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2017.09.017 – volume: 169 start-page: 132 issue: 2 year: 2009 ident: 3689_CR9 publication-title: Psychiatry Res doi: 10.1016/j.psychres.2009.04.004 – ident: 3689_CR28 doi: 10.1109/CVPR.2005.254 – ident: 3689_CR4 – volume: 40 start-page: 263 issue: 1 year: 2013 ident: 3689_CR31 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2012.07.014 – volume: 2014 start-page: 1 year: 2014 ident: 3689_CR33 publication-title: Comput Math Methods Med doi: 10.1155/2014/985789 – volume: 7 start-page: 351 year: 2013 ident: 3689_CR6 publication-title: Cogn Neurodyn doi: 10.1007/s11571-013-9247-z – volume: 33 start-page: 42 issue: 1 year: 2009 ident: 3689_CR12 publication-title: J Med Eng Technol doi: 10.1080/03091900701559408 – volume: 13 start-page: 1 year: 2018 ident: 3689_CR20 publication-title: Appl Intell |
| SSID | ssj0004685 |
| Score | 2.6625535 |
| Snippet | An automated detection system for Parkinson’s disease (PD) employing the convolutional neural network (CNN) is proposed in this study. PD is characterized by... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 10927 |
| SubjectTerms | Artificial Intelligence Artificial neural networks Automation Brain Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Deep learning Diagnosis Disease Dopamine Electroencephalography Engineering Epilepsy Image Processing and Computer Vision Machine learning Medical diagnosis Mental disorders Neural networks Neurons Parkinson's disease Probability and Statistics in Computer Science S.I. : Computer aided Medical Diagnosis |
| SummonAdditionalLinks | – databaseName: ProQuest Central dbid: BENPR link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1PS8MwFH_o5sGL8y9Op-TgSQl2bbK0J5my6UHHEJXdStI2MpBtrtOzX8Ov5yfxJUs3Fd1FCJSQhoS8l-T98v4BHAmRoFCcapoInlDGdURDhbzssdTEO1daWgPZh2vR6YS9XtR1D265M6sszkR7UKfDxLyRn_pWRWfK2eiZmqxRRrvqUmgsQ9lEKmMlKJ-3Ot3bL56RNiknYhhj38OCQq_p2TCieHUhlA4pTiqi_PvNNBc3f2hI7cXTrvx3yuuw5kRO0pzyyAYsZYNNqBTpHIjb3Vtw0yRplo2ISyTxSIp44wQFW2Lco62n2Mfbe06cXge_1lKvnxPjp0JarUtiLEKQp7fhvt26u7iiLtsCTVjdm9B6Q7FAcC59pQKsBCkXQoa-ZFFd6YZA4CN4yrBomWkdaD-NNFYMxskkbwQ7UBoMB9kukIR5SnuSC183sLcMw0hJRG4S8Ylika6CV6x0nLhQ5CYjxlM8C6JsiRMjcWJDnJhX4XjWZTSNw7Ho51pBkNhtyTyeU-PX5gBlHc5QmqtX4aSg-Lz5z7H2Fo-1D6u-QejWZLAGpcn4JTuAleR10s_Hh45dPwGZ3u94 priority: 102 providerName: ProQuest |
| Title | A deep learning approach for Parkinson’s disease diagnosis from EEG signals |
| URI | https://link.springer.com/article/10.1007/s00521-018-3689-5 https://www.proquest.com/docview/2424564564 https://www.proquest.com/docview/3218547271 |
| Volume | 32 |
| WOSCitedRecordID | wos000549646700010&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1433-3058 dateEnd: 20241207 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: P5Z dateStart: 20120101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central (New) customDbUrl: eissn: 1433-3058 dateEnd: 20241207 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: BENPR dateStart: 20120101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1433-3058 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1bS8MwFD64zQdfnFeczpEHn5RCL0nTPk7p9EHHmDqGLyVpGxnIHOv02b_h3_OXeJK188IUFAIl5FZOTprz9dwAjjhPUChOlZVwlliUqdAKJPKyTVMd71wqYQxkB5e82w2Gw7BX-HHnpbV7qZI0X-qFs5v-g6mhb2DhJKHFKlDD2y7Qp7F_PfjkDGnycCJs0SY91CtVmcum-HoZfUiY35Si5q7p1P_1lhuwXoiWpD3nhU1YycZbUC_TNpDiFG_DVZukWTYhRcKIe1LGFScowBLtBm08wt5eXnNS6G_waSzyRjnR_igkis6JtvxA3t2B2050c3ZhFVkVrIQ69sxyfEk9zphwpfSw4qWMcxG4goaOVD5HgMNZSrEokSnlKTcNFVY0lskE871dqI4fx9kekITaUtmCcVf5OFoEQSgFIjSBOETSUDXALskbJ0XIcZ354iFeBEs25IqRXLEmV8wacLwYMpnH2_itc7Pcs7g4ennsGl2uLkubPZRpGEWpzWnASbmFH80_rrX_p94HsOZqYG4sBZtQnU2fskNYTZ5no3zagtpp1O31W1DpsbuWYd93qw3mNw |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LS8QwEB58gV58i-szB70owW6bbNqDiOj6wHXxoCJeatI2siDralfFm3_DP-GP8pc4yTauinrzIARKSNPQZCb5JvMCWBIiQVCcapoInlDGdURDhbTssdTEO1daWgPZ05qo18Ozs-ioB16cL4wxq3R7ot2o0-vE3JGv-VZFZ8pG64aarFFGu-pSaHTI4iB7fECRLV_f38b1Xfb9nerx1h4tsgrQhJW9Ni1XFAsE59JXKsBKkHIhZOhLFpWVrggE-IKnDIuWmdaB9tNIY8Vg-UzySoDf7YV-xlBYQv454ucf_DBtClCUmIw1EQucFtWzQUvxoETBPaQ4BRHln8_BLrj9oo-1x9zOyH-boFEYLgA12exwwBj0ZM1xGHHJKkixd03A4SZJs6xFijQZl8RFUycI24lx_rZ-cK9PzzkptFb4tHaIjZwYLxxSre4SY--CHDsJJ3_yU1PQ17xuZtNAEuYp7UkufF3B3jIMIyVRLpUofSkW6RJ4bmXjpAi0bvJ9XMXvIaItMcRIDLEhhpiXYOW9S6sTZeS3l-ccAcTFhpPH3dX_tjlAJMcZYtVyCVYdhXWbfxxr5vexFmFw7_iwFtf26wezMOSbuwhrHDkHfe3bu2weBpL7diO_XbCMQuDirwnvDbWFStQ |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bS8MwFD7oFPHFecXp1Dz4pJRtbdK0j0M3FecYqGNvJWkbGUgda_XZv-Hf85d4krWbyhRECJSQS8tJQs7Xc_kAjjkPUSmOlBVyFlqUKd_yJO7lOo10vnOphHGQ7Xd4t-sNBn4v5zlNC2_3wiQ5iWnQWZqSrDaKVG0a-Kb_ZmoY7Fk4oW-xRViimjNIw_Xb_qfASMPJiRBGu_dQpzBrzpvi68U00za_GUjNvdMu__uL12EtVzlJc7JHNmAhTjahXNA5kPx0b8FNk0RxPCI5kcQDKfKNE1RsiQ6PNpFi769vKcntOvg0nnrDlOg4FdJqXRDtEYJ7ehvu2627s0srZ1uwQhRdZjVcSR3OmLCldLDiRIxz4dmC-g2pXI7Ah7OIYlEiVspRduQrrGiMEwvmOjtQSp6SeBdISOtS1QXjtnJxtPA8XwpEbgLxiaS-qkC9EHUQ5qnINSPGYzBNomzEFaC4Ai2ugFXgZDpkNMnD8VvnarF-QX4k08A2Nl5d5jY7qOswitpcowKnxXLOmn98196feh_BSu-8HXSuutf7sGpr7G6cCatQysbP8QEshy_ZMB0fmn38AQQF72U |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+deep+learning+approach+for+Parkinson%E2%80%99s+disease+diagnosis+from+EEG+signals&rft.jtitle=Neural+computing+%26+applications&rft.au=Oh%2C+Shu+Lih&rft.au=Hagiwara%2C+Yuki&rft.au=Raghavendra%2C+U.&rft.au=Yuvaraj%2C+Rajamanickam&rft.date=2020-08-01&rft.pub=Springer+London&rft.issn=0941-0643&rft.eissn=1433-3058&rft.volume=32&rft.issue=15&rft.spage=10927&rft.epage=10933&rft_id=info:doi/10.1007%2Fs00521-018-3689-5&rft.externalDocID=10_1007_s00521_018_3689_5 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0941-0643&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0941-0643&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0941-0643&client=summon |