Analysis of in-air movement in handwriting: A novel marker for Parkinson's disease
•We exploit the fact that movement during handwriting of a text consists of on-surface and in-air movements and use it for differential diagnosis of PD.•By applying feature selection algorithms and support vector machine learning methods to separate PD from HC, we demonstrated 85% classification acc...
Saved in:
| Published in | Computer methods and programs in biomedicine Vol. 117; no. 3; pp. 405 - 411 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Kidlington
Elsevier Ireland Ltd
01.12.2014
Elsevier |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | •We exploit the fact that movement during handwriting of a text consists of on-surface and in-air movements and use it for differential diagnosis of PD.•By applying feature selection algorithms and support vector machine learning methods to separate PD from HC, we demonstrated 85% classification accuracy.•Decision support system based on handwriting analysis can be complementary method to diagnosis made by clinician or other decision support systems.
Parkinson's disease (PD) is the second most common neurodegenerative disease affecting significant portion of elderly population. One of the most frequent hallmarks and usually also the first manifestation of PD is deterioration of handwriting characterized by micrographia and changes in kinematics of handwriting. There is no objective quantitative method of clinical diagnosis of PD. It is thought that PD can only be definitively diagnosed at postmortem, which further highlights the complexities of diagnosis.
We exploit the fact that movement during handwriting of a text consists not only from the on-surface movements of the hand, but also from the in-air trajectories performed when the hand moves in the air from one stroke to the next. We used a digitizing tablet to assess both in-air and on-surface kinematic variables during handwriting of a sentence in 37 PD patients on medication and 38 age- and gender-matched healthy controls.
By applying feature selection algorithms and support vector machine learning methods to separate PD patients from healthy controls, we demonstrated that assessing the in-air/on-surface hand movements led to accurate classifications in 84% and 78% of subjects, respectively. Combining both modalities improved the accuracy by another 1% over the evaluation of in-air features alone and provided medically relevant diagnosis with 85.61% prediction accuracy.
Assessment of in-air movements during handwriting has a major impact on disease classification accuracy. This study confirms that handwriting can be used as a marker for PD and can be with advance used in decision support systems for differential diagnosis of PD. |
|---|---|
| AbstractList | Background and objective: Parkinson's disease (PD) is the second most common neurodegenerative disease affecting significant portion of elderly population. One of the most frequent hallmarks and usually also the first manifestation of PD is deterioration of handwriting characterized by micrographia and changes in kinematics of handwriting. There is no objective quantitative method of clinical diagnosis of PD. It is thought that PD can only be definitively diagnosed at postmortem, which further highlights the complexities of diagnosis. Methods: We exploit the fact that movement during handwriting of a text consists not only from the on-surface movements of the hand, but also from the in-air trajectories performed when the hand moves in the air from one stroke to the next. We used a digitizing tablet to assess both in-air and on-surface kinematic variables during handwriting of a sentence in 37 PD patients on medication and 38 age- and gender-matched healthy controls. Results: By applying feature selection algorithms and support vector machine learning methods to separate PD patients from healthy controls, we demonstrated that assessing the in-air/on-surface hand movements led to accurate classifications in 84% and 78% of subjects, respectively. Combining both modalities improved the accuracy by another 1% over the evaluation of in-air features alone and provided medically relevant diagnosis with 85.61% prediction accuracy. Conclusions: Assessment of in-air movements during handwriting has a major impact on disease classification accuracy. This study confirms that handwriting can be used as a marker for PD and can be with advance used in decision support systems for differential diagnosis of PD. Parkinson's disease (PD) is the second most common neurodegenerative disease affecting significant portion of elderly population. One of the most frequent hallmarks and usually also the first manifestation of PD is deterioration of handwriting characterized by micrographia and changes in kinematics of handwriting. There is no objective quantitative method of clinical diagnosis of PD. It is thought that PD can only be definitively diagnosed at postmortem, which further highlights the complexities of diagnosis.BACKGROUND AND OBJECTIVEParkinson's disease (PD) is the second most common neurodegenerative disease affecting significant portion of elderly population. One of the most frequent hallmarks and usually also the first manifestation of PD is deterioration of handwriting characterized by micrographia and changes in kinematics of handwriting. There is no objective quantitative method of clinical diagnosis of PD. It is thought that PD can only be definitively diagnosed at postmortem, which further highlights the complexities of diagnosis.We exploit the fact that movement during handwriting of a text consists not only from the on-surface movements of the hand, but also from the in-air trajectories performed when the hand moves in the air from one stroke to the next. We used a digitizing tablet to assess both in-air and on-surface kinematic variables during handwriting of a sentence in 37 PD patients on medication and 38 age- and gender-matched healthy controls.METHODSWe exploit the fact that movement during handwriting of a text consists not only from the on-surface movements of the hand, but also from the in-air trajectories performed when the hand moves in the air from one stroke to the next. We used a digitizing tablet to assess both in-air and on-surface kinematic variables during handwriting of a sentence in 37 PD patients on medication and 38 age- and gender-matched healthy controls.By applying feature selection algorithms and support vector machine learning methods to separate PD patients from healthy controls, we demonstrated that assessing the in-air/on-surface hand movements led to accurate classifications in 84% and 78% of subjects, respectively. Combining both modalities improved the accuracy by another 1% over the evaluation of in-air features alone and provided medically relevant diagnosis with 85.61% prediction accuracy.RESULTSBy applying feature selection algorithms and support vector machine learning methods to separate PD patients from healthy controls, we demonstrated that assessing the in-air/on-surface hand movements led to accurate classifications in 84% and 78% of subjects, respectively. Combining both modalities improved the accuracy by another 1% over the evaluation of in-air features alone and provided medically relevant diagnosis with 85.61% prediction accuracy.Assessment of in-air movements during handwriting has a major impact on disease classification accuracy. This study confirms that handwriting can be used as a marker for PD and can be with advance used in decision support systems for differential diagnosis of PD.CONCLUSIONSAssessment of in-air movements during handwriting has a major impact on disease classification accuracy. This study confirms that handwriting can be used as a marker for PD and can be with advance used in decision support systems for differential diagnosis of PD. •We exploit the fact that movement during handwriting of a text consists of on-surface and in-air movements and use it for differential diagnosis of PD.•By applying feature selection algorithms and support vector machine learning methods to separate PD from HC, we demonstrated 85% classification accuracy.•Decision support system based on handwriting analysis can be complementary method to diagnosis made by clinician or other decision support systems. Parkinson's disease (PD) is the second most common neurodegenerative disease affecting significant portion of elderly population. One of the most frequent hallmarks and usually also the first manifestation of PD is deterioration of handwriting characterized by micrographia and changes in kinematics of handwriting. There is no objective quantitative method of clinical diagnosis of PD. It is thought that PD can only be definitively diagnosed at postmortem, which further highlights the complexities of diagnosis. We exploit the fact that movement during handwriting of a text consists not only from the on-surface movements of the hand, but also from the in-air trajectories performed when the hand moves in the air from one stroke to the next. We used a digitizing tablet to assess both in-air and on-surface kinematic variables during handwriting of a sentence in 37 PD patients on medication and 38 age- and gender-matched healthy controls. By applying feature selection algorithms and support vector machine learning methods to separate PD patients from healthy controls, we demonstrated that assessing the in-air/on-surface hand movements led to accurate classifications in 84% and 78% of subjects, respectively. Combining both modalities improved the accuracy by another 1% over the evaluation of in-air features alone and provided medically relevant diagnosis with 85.61% prediction accuracy. Assessment of in-air movements during handwriting has a major impact on disease classification accuracy. This study confirms that handwriting can be used as a marker for PD and can be with advance used in decision support systems for differential diagnosis of PD. Parkinson's disease (PD) is the second most common neurodegenerative disease affecting significant portion of elderly population. One of the most frequent hallmarks and usually also the first manifestation of PD is deterioration of handwriting characterized by micrographia and changes in kinematics of handwriting. There is no objective quantitative method of clinical diagnosis of PD. It is thought that PD can only be definitively diagnosed at postmortem, which further highlights the complexities of diagnosis. We exploit the fact that movement during handwriting of a text consists not only from the on-surface movements of the hand, but also from the in-air trajectories performed when the hand moves in the air from one stroke to the next. We used a digitizing tablet to assess both in-air and on-surface kinematic variables during handwriting of a sentence in 37 PD patients on medication and 38 age- and gender-matched healthy controls. By applying feature selection algorithms and support vector machine learning methods to separate PD patients from healthy controls, we demonstrated that assessing the in-air/on-surface hand movements led to accurate classifications in 84% and 78% of subjects, respectively. Combining both modalities improved the accuracy by another 1% over the evaluation of in-air features alone and provided medically relevant diagnosis with 85.61% prediction accuracy. Assessment of in-air movements during handwriting has a major impact on disease classification accuracy. This study confirms that handwriting can be used as a marker for PD and can be with advance used in decision support systems for differential diagnosis of PD. Highlights • We exploit the fact that movement during handwriting of a text consists of on-surface and in-air movements and use it for differential diagnosis of PD. • By applying feature selection algorithms and support vector machine learning methods to separate PD from HC, we demonstrated 85% classification accuracy. • Decision support system based on handwriting analysis can be complementary method to diagnosis made by clinician or other decision support systems. |
| Author | Faundez-Zanuy, Marcos Mekyska, Jiří Drotár, Peter Rektorová, Irena Masarová, Lucia Smékal, Zdenek |
| Author_xml | – sequence: 1 givenname: Peter surname: Drotár fullname: Drotár, Peter organization: Brno University of Technology, Technicka 12, Brno, Czech Republic – sequence: 2 givenname: Jiří surname: Mekyska fullname: Mekyska, Jiří organization: Brno University of Technology, Technicka 12, Brno, Czech Republic – sequence: 3 givenname: Irena surname: Rektorová fullname: Rektorová, Irena email: irena.rektorova@fnusa.cz organization: First Department of Neurology, Masaryk University and St. Anne's Hospital, Pekarska 664, 656 91 Brno, Czech Republic – sequence: 4 givenname: Lucia surname: Masarová fullname: Masarová, Lucia organization: First Department of Neurology, Masaryk University and St. Anne's Hospital, Pekarska 664, 656 91 Brno, Czech Republic – sequence: 5 givenname: Zdenek surname: Smékal fullname: Smékal, Zdenek organization: Brno University of Technology, Technicka 12, Brno, Czech Republic – sequence: 6 givenname: Marcos orcidid: 0000-0003-0605-1282 surname: Faundez-Zanuy fullname: Faundez-Zanuy, Marcos organization: Tecnocampus, Av. Ernest Lluch, 32, 08302 Mataro, Barcelona, Spain |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28915509$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/25261003$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkl9rFDEUxYNU7Lb6BXyQvIi-zHiTmcwkRYSl2CoUFP88h2zmjmY7k6zJbGW_vRl2i1Cw9ikJ-Z0Tcs49IUc-eCTkOYOSAWverEs7blYlB1aXIEuA9hFZMNnyohWNOCKLDKmCN9Aek5OU1gDAhWiekGMueMMAqgX5svRm2CWXaOip84VxkY7hBkf0Uz7Tn8Z3v6ObnP9xRpfU56uBjiZeY6R9iPRz3jqfgn-VaOcSmoRPyePeDAmfHdZT8v3i_bfzD8XVp8uP58urwgohp0JxWTMjsOWV7dSK1YYhWwE2vIe6b61CqUzb9ateyRYNKNs1jPHOWMvqXmJ1Sl7vfTcx_NpimvToksVhMB7DNmnWCFZX-ZfVA9BKQM1bCQ9AuVINSNlk9MUB3a5G7PQmupzMTt-mm4GXB8Aka4Y-Gm9d-stJxYQAlTm552wMKUXstXWTmVzwUzRu0Az0XLhe67lwPReuQepceJbyO9Jb93tFb_cizPXcOIw6WYfeYuci2kl3wd0vf3dHbgfnXf7hNe4wrcM25qHKUenENeiv8xjOU8jqHAqHOhuc_dvgf6__ATVq6nc |
| CitedBy_id | crossref_primary_10_1142_S0218001424400019 crossref_primary_10_1109_LSP_2024_3496579 crossref_primary_10_1016_j_artmed_2018_04_010 crossref_primary_10_1016_j_bspc_2022_104198 crossref_primary_10_1109_ACCESS_2023_3337804 crossref_primary_10_1016_j_forsciint_2018_08_021 crossref_primary_10_1007_s00521_019_04069_0 crossref_primary_10_1142_S2196888821500238 crossref_primary_10_1007_s11042_024_19314_5 crossref_primary_10_1155_2018_7613282 crossref_primary_10_3233_JIFS_221708 crossref_primary_10_1016_j_bspc_2023_104748 crossref_primary_10_1016_j_eswa_2023_120740 crossref_primary_10_1371_journal_pone_0263159 crossref_primary_10_3390_app9214666 crossref_primary_10_3389_frai_2022_787179 crossref_primary_10_1007_s11042_022_13759_2 crossref_primary_10_3390_app13063603 crossref_primary_10_1007_s11042_023_15811_1 crossref_primary_10_1007_s40747_024_01507_y crossref_primary_10_1016_j_mlwa_2024_100553 crossref_primary_10_1007_s00415_024_12611_x crossref_primary_10_1515_bmt_2017_0148 crossref_primary_10_1016_j_bspc_2023_105436 crossref_primary_10_1007_s00521_021_06626_y crossref_primary_10_1007_s10072_024_07734_y crossref_primary_10_1016_j_bspc_2022_103551 crossref_primary_10_1007_s00521_020_04735_8 crossref_primary_10_1007_s00521_022_07185_6 crossref_primary_10_1016_j_cmpb_2019_105305 crossref_primary_10_3389_fmed_2024_1453743 crossref_primary_10_1007_s13534_023_00319_2 crossref_primary_10_1371_journal_pone_0286506 crossref_primary_10_3934_math_2024334 crossref_primary_10_1016_j_eswa_2022_117400 crossref_primary_10_1007_s12652_022_04361_3 crossref_primary_10_1002_mdc3_12552 crossref_primary_10_1016_j_arr_2024_102285 crossref_primary_10_3390_app10103479 crossref_primary_10_1016_j_jneumeth_2020_108727 crossref_primary_10_1109_RBME_2018_2840679 crossref_primary_10_3389_fnagi_2021_633752 crossref_primary_10_3390_s23042085 crossref_primary_10_1016_j_bbe_2021_12_007 crossref_primary_10_1016_j_biosystems_2023_105006 crossref_primary_10_1016_j_cmpb_2024_108066 crossref_primary_10_1016_j_engappai_2020_103955 crossref_primary_10_1109_ACCESS_2019_2932037 crossref_primary_10_17706_ijbbb_2018_8_4_202_209 crossref_primary_10_3390_computers12030058 crossref_primary_10_1016_j_future_2023_03_033 crossref_primary_10_1016_j_patcog_2021_108513 crossref_primary_10_1007_s42979_024_03250_0 crossref_primary_10_1016_j_future_2020_11_020 crossref_primary_10_1016_j_patrec_2018_04_008 crossref_primary_10_1109_THMS_2016_2586605 crossref_primary_10_1016_j_neucom_2025_129450 crossref_primary_10_1049_ipr2_12820 crossref_primary_10_1016_j_parkreldis_2021_11_037 crossref_primary_10_3390_electronics13193962 crossref_primary_10_1016_j_cmpb_2019_03_005 crossref_primary_10_1371_journal_pone_0270466 crossref_primary_10_1016_j_eswa_2020_114405 crossref_primary_10_1109_TSMC_2020_3048892 crossref_primary_10_1016_j_artmed_2021_102081 crossref_primary_10_1007_s00521_021_06469_7 crossref_primary_10_3389_fninf_2022_877139 crossref_primary_10_1080_02648725_2023_2200333 crossref_primary_10_1109_ACCESS_2021_3119035 crossref_primary_10_3390_info10010030 crossref_primary_10_3390_e18040115 crossref_primary_10_1016_j_measen_2021_100047 crossref_primary_10_1016_j_compbiomed_2017_01_004 crossref_primary_10_1007_s12559_020_09755_z crossref_primary_10_1007_s42486_024_00170_z crossref_primary_10_3389_fneur_2019_01259 crossref_primary_10_1007_s12559_019_09642_2 crossref_primary_10_1016_j_cmpb_2016_08_005 crossref_primary_10_1007_s42452_019_1923_0 |
| Cites_doi | 10.1006/exnr.1997.6507 10.1016/j.cmpb.2012.10.016 10.1007/s00415-013-6996-x 10.1002/mds.23680 10.1023/A:1018628609742 10.1109/TPAMI.2005.159 10.1016/j.neubiorev.2013.01.015 10.1136/jnnp.63.4.429 10.1007/s12559-011-9119-y 10.1109/TBME.2012.2183367 10.1016/j.jneumeth.2013.11.016 10.1016/j.ridd.2013.09.012 10.1098/rsif.2010.0456 10.1093/brain/120.1.91 10.1016/j.ridd.2012.02.010 10.4316/AECE.2014.01020 10.1007/s00221-009-1925-z 10.1109/TITB.2009.2036165 10.1016/j.brainresbull.2003.11.005 10.1016/j.cmpb.2013.09.007 10.1016/j.cmpb.2014.01.004 10.1002/mds.23429 10.1007/s00702-005-0346-9 10.1109/TNSRE.2008.918422 10.1002/mds.870110313 10.1016/j.humov.2006.07.005 10.1016/j.cmpb.2013.04.016 10.1016/j.cmpb.2013.09.011 |
| ContentType | Journal Article |
| Copyright | 2014 Elsevier Ireland Ltd Elsevier Ireland Ltd 2015 INIST-CNRS Copyright © 2014 Elsevier Ireland Ltd. All rights reserved. |
| Copyright_xml | – notice: 2014 Elsevier Ireland Ltd – notice: Elsevier Ireland Ltd – notice: 2015 INIST-CNRS – notice: Copyright © 2014 Elsevier Ireland Ltd. All rights reserved. |
| DBID | AAYXX CITATION IQODW CGR CUY CVF ECM EIF NPM 7X8 7QO 8FD FR3 P64 7SC JQ2 L7M L~C L~D |
| DOI | 10.1016/j.cmpb.2014.08.007 |
| DatabaseName | CrossRef Pascal-Francis Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic Biotechnology Research Abstracts Technology Research Database Engineering Research Database Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic Engineering Research Database Biotechnology Research Abstracts Technology Research Database Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Engineering Research Database MEDLINE - Academic Computer and Information Systems Abstracts MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Applied Sciences |
| EISSN | 1872-7565 |
| EndPage | 411 |
| ExternalDocumentID | 25261003 28915509 10_1016_j_cmpb_2014_08_007 S0169260714003204 1_s2_0_S0169260714003204 |
| Genre | Research Support, Non-U.S. Gov't Journal Article |
| GroupedDBID | --- --K --M -~X .1- .DC .FO .GJ .~1 0R~ 1B1 1P~ 1RT 1~. 1~5 29F 4.4 457 4G. 53G 5GY 5RE 5VS 7-5 71M 8P~ 9JN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AATTM AAXKI AAXUO AAYFN AAYWO ABBOA ABFNM ABJNI ABMAC ABMZM ABWVN ABXDB ACDAQ ACGFS ACIEU ACIUM ACNNM ACRLP ACRPL ACVFH ACZNC ADBBV ADCNI ADEZE ADJOM ADMUD ADNMO AEBSH AEIPS AEKER AENEX AEUPX AEVXI AFJKZ AFPUW AFRHN AFTJW AFXIZ AGCQF AGHFR AGQPQ AGUBO AGYEJ AHHHB AHZHX AIALX AIEXJ AIGII AIIUN AIKHN AITUG AJRQY AJUYK AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU ANZVX AOUOD APXCP ASPBG AVWKF AXJTR AZFZN BKOJK BLXMC BNPGV CS3 DU5 EBS EFJIC EFKBS EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA GBOLZ HLZ HMK HMO HVGLF HZ~ IHE J1W KOM LG9 M29 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- ROL RPZ SAE SBC SDF SDG SEL SES SEW SPC SPCBC SSH SSV SSZ T5K UHS WUQ XPP Z5R ZGI ZY4 ~G- AFCTW AGRNS RIG AACTN AAIAV ABLVK ABTAH ABYKQ AFKWA AJBFU AJOXV AMFUW EFLBG LCYCR AAYXX CITATION IQODW CGR CUY CVF ECM EIF NPM 7X8 7QO 8FD FR3 P64 7SC JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c558t-92841a5e723cd9b14a1e1b0e62f04f7c9e89a7dfbf987ea09cd6112dacc14f8e3 |
| IEDL.DBID | .~1 |
| ISSN | 0169-2607 1872-7565 |
| IngestDate | Mon Jul 21 10:01:18 EDT 2025 Mon Jul 21 09:52:47 EDT 2025 Fri Jul 11 02:53:44 EDT 2025 Thu Apr 03 07:09:52 EDT 2025 Wed Apr 02 07:13:53 EDT 2025 Thu Apr 24 23:07:42 EDT 2025 Tue Jul 01 02:40:42 EDT 2025 Fri Feb 23 02:26:00 EST 2024 Fri May 16 00:31:31 EDT 2025 Tue Aug 26 16:33:36 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Keywords | Decision support systems Handwriting In-air movement Parkinson's disease Disease classification Micrographia Human Computer vision Cognitive disorder Motion estimation Decision support system Parkinson disease Text Marker Hand Kinematics Body movement Vector support machine Diagnosis Manuscript character Elderly Sentence Age Micrography |
| Language | English |
| License | CC BY 4.0 Copyright © 2014 Elsevier Ireland Ltd. All rights reserved. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c558t-92841a5e723cd9b14a1e1b0e62f04f7c9e89a7dfbf987ea09cd6112dacc14f8e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0003-0605-1282 |
| PMID | 25261003 |
| PQID | 1629960886 |
| PQPubID | 23479 |
| PageCount | 7 |
| ParticipantIDs | proquest_miscellaneous_1651430033 proquest_miscellaneous_1635042780 proquest_miscellaneous_1629960886 pubmed_primary_25261003 pascalfrancis_primary_28915509 crossref_citationtrail_10_1016_j_cmpb_2014_08_007 crossref_primary_10_1016_j_cmpb_2014_08_007 elsevier_sciencedirect_doi_10_1016_j_cmpb_2014_08_007 elsevier_clinicalkeyesjournals_1_s2_0_S0169260714003204 elsevier_clinicalkey_doi_10_1016_j_cmpb_2014_08_007 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2014-12-01 |
| PublicationDateYYYYMMDD | 2014-12-01 |
| PublicationDate_xml | – month: 12 year: 2014 text: 2014-12-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | Kidlington |
| PublicationPlace_xml | – name: Kidlington – name: Ireland |
| PublicationTitle | Computer methods and programs in biomedicine |
| PublicationTitleAlternate | Comput Methods Programs Biomed |
| PublicationYear | 2014 |
| Publisher | Elsevier Ireland Ltd Elsevier |
| Publisher_xml | – name: Elsevier Ireland Ltd – name: Elsevier |
| References | Pirooznia, Yang, Yang, Deng (bib0155) 2008; 9 Rusz, Cmejla, Ruicková, Klempír, Majerová, Picmausová, Roth, Ruicka (bib0070) 2011; 26 Salvatore, Cerasa, Castiglioni, Gallivanone, Augimeri, Lopez, Arabia, Morelli, Gilardi, Quattrone (bib0185) 2014; 222 Vapnik (bib0135) 1998 Nackaerts, Vervoort, Heremans, Smits-Engelsman, Swinnen, Nieuwboer (bib0010) 2013; 37 Sesa-Nogueras, Faundez-Zanuy, Mekyska (bib0050) 2012; 4 Eichhorn, Gasser, Mai, Marquardt, Arnold, Schwarz, Oertel (bib0015) 1996; 11 Folfstein, Folfstein, McHugh (bib0105) 1975; 12 Tripoliti, Tzallas, Tsipouras, Rigas, Bougia, Leontiou, Konitsiotis, Chondrogiorgi, Tsouli, Fotiadis (bib0080) 2013; 110 Tomlinson, Stowe, Patel, Rick, Gray, Clarke (bib0100) 2010; 25 Hastie, Tibshirani, Friedman (bib0140) 2009 Jovanov, Wang, Verhagen, Fredrickson, Fratangelo (bib0085) 2009 Hariharan, Polat, Sindhu (bib0065) 2014; 113 Sun, Wang, Li, Lv, Liu, Luo, Gao, Zhu, Guo (bib0160) 2013; 111 Bächlin, Plotnik, Roggen, Maidan, Hausdorff, Giladi, Troster (bib0075) 2010; 14 Teulings, Contreras-Vidal, Stelmach, Adler (bib0005) 1997; 146 Danna, Paz-Villagrán, Velay (bib0035) 2013; 34 Rosenblum, Dvorkin, Weiss (bib0030) 2006; 25 Oliveira, Gurd, Nixon, Marshall, Passingham (bib0180) 1997; 63 Rosenblum, Samuel, Zlotnik, Erikh, Schlesinger (bib0055) 2013; 260 Tsanas, Little, McSharry, Ramig (bib0115) 2011 Sheskin (bib0120) 2000 Shen, Lee, Chen (bib0040) 2012; 33 Fahn, Elton, Members of the UPDRS Development Committee (bib0095) 1987 Badesa, Morales, Garcia-Aracil, Sabater, Casals, Zollo (bib0175) 2014; 116 Cao, Tutwiler, Slobounov (bib0170) 2008; 16 Tucha, Mecklinger, Thome, Reiter, Alders, Sartor, Naumann, Lange (bib0025) 2006; 113 Geman, Costin (bib0090) 2014; 14 Forbes, Shanks, Venneri (bib0045) 2004; 63 Suykens, Vandewalle (bib0145) 1999; 9 Cover (bib0125) 1991 Weiss, Stelmach, Hefter (bib0165) 1997; 120 Broderick, Van Gemmert, Shill, Stelmach (bib0020) 2009; 197 Yin, Zhang (bib0150) 2014; 113 Tsanas, Little, McSharry, Spielman, Ramig (bib0060) 2012; 59 Drotár, Mekyska, Rektorová, Masarová, Smékal, Faundez-Zanuy (bib0110) 2013 Peng, Long, Ding (bib0130) 2005; 27 Tripoliti (10.1016/j.cmpb.2014.08.007_bib0080) 2013; 110 Rusz (10.1016/j.cmpb.2014.08.007_bib0070) 2011; 26 Geman (10.1016/j.cmpb.2014.08.007_bib0090) 2014; 14 Tomlinson (10.1016/j.cmpb.2014.08.007_bib0100) 2010; 25 Shen (10.1016/j.cmpb.2014.08.007_bib0040) 2012; 33 Danna (10.1016/j.cmpb.2014.08.007_bib0035) 2013; 34 Folfstein (10.1016/j.cmpb.2014.08.007_bib0105) 1975; 12 Tsanas (10.1016/j.cmpb.2014.08.007_bib0115) 2011 Cao (10.1016/j.cmpb.2014.08.007_bib0170) 2008; 16 Tsanas (10.1016/j.cmpb.2014.08.007_bib0060) 2012; 59 Suykens (10.1016/j.cmpb.2014.08.007_bib0145) 1999; 9 Rosenblum (10.1016/j.cmpb.2014.08.007_bib0055) 2013; 260 Pirooznia (10.1016/j.cmpb.2014.08.007_bib0155) 2008; 9 Badesa (10.1016/j.cmpb.2014.08.007_bib0175) 2014; 116 Yin (10.1016/j.cmpb.2014.08.007_bib0150) 2014; 113 Broderick (10.1016/j.cmpb.2014.08.007_bib0020) 2009; 197 Forbes (10.1016/j.cmpb.2014.08.007_bib0045) 2004; 63 Sun (10.1016/j.cmpb.2014.08.007_bib0160) 2013; 111 Sheskin (10.1016/j.cmpb.2014.08.007_bib0120) 2000 Oliveira (10.1016/j.cmpb.2014.08.007_bib0180) 1997; 63 Eichhorn (10.1016/j.cmpb.2014.08.007_bib0015) 1996; 11 Peng (10.1016/j.cmpb.2014.08.007_bib0130) 2005; 27 Nackaerts (10.1016/j.cmpb.2014.08.007_bib0010) 2013; 37 Jovanov (10.1016/j.cmpb.2014.08.007_bib0085) 2009 Tucha (10.1016/j.cmpb.2014.08.007_bib0025) 2006; 113 Cover (10.1016/j.cmpb.2014.08.007_bib0125) 1991 Sesa-Nogueras (10.1016/j.cmpb.2014.08.007_bib0050) 2012; 4 Salvatore (10.1016/j.cmpb.2014.08.007_bib0185) 2014; 222 Drotár (10.1016/j.cmpb.2014.08.007_bib0110) 2013 Hariharan (10.1016/j.cmpb.2014.08.007_bib0065) 2014; 113 Hastie (10.1016/j.cmpb.2014.08.007_bib0140) 2009 Fahn (10.1016/j.cmpb.2014.08.007_bib0095) 1987 Teulings (10.1016/j.cmpb.2014.08.007_bib0005) 1997; 146 Bächlin (10.1016/j.cmpb.2014.08.007_bib0075) 2010; 14 Weiss (10.1016/j.cmpb.2014.08.007_bib0165) 1997; 120 Rosenblum (10.1016/j.cmpb.2014.08.007_bib0030) 2006; 25 Vapnik (10.1016/j.cmpb.2014.08.007_bib0135) 1998 |
| References_xml | – volume: 116 start-page: 123 year: 2014 end-page: 130 ident: bib0175 article-title: Auto-adaptive robot-aided therapy using machine learning techniques publication-title: Comput. Methods Prog. Biomed. – volume: 4 start-page: 195 year: 2012 end-page: 205 ident: bib0050 article-title: An information analysis of in-air and on-surface trajectories in online handwriting publication-title: Cogn. Comput. – volume: 14 start-page: 436 year: 2010 end-page: 446 ident: bib0075 article-title: Wearable assistant for Parkinson's disease patients with the freezing of gait symptom publication-title: IEEE Trans. Inf. Technol. Biomed. – volume: 9 start-page: 293 year: 1999 end-page: 300 ident: bib0145 article-title: Least squares support vector machine classifiers publication-title: Neural Process. Lett. – year: 1998 ident: bib0135 article-title: Statistical Learning Theory – volume: 113 start-page: 609 year: 2006 end-page: 623 ident: bib0025 article-title: Kinematic analysis of dopaminergic effects on skilled handwriting movements in Parkinson's disease publication-title: J. Neural Transm. – volume: 11 start-page: 289 year: 1996 end-page: 297 ident: bib0015 article-title: Computational analysis of open loop handwriting movements in Parkinson's disease: a rapid method to detect dopamimetic effects publication-title: Mov. Disord. – volume: 25 start-page: 2649 year: 2010 end-page: 2653 ident: bib0100 article-title: Systematic review of levodopa dose equivalency reporting in Parkinson's disease publication-title: Mov. Disord. – start-page: 842 year: 2011 end-page: 855 ident: bib0115 article-title: Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity publication-title: J. R. Soc. Interface – start-page: 153 year: 1987 end-page: 163 ident: bib0095 article-title: Unified Parkinson's disease rating scale publication-title: Recent developments in Parkinson's disease. – volume: 12 start-page: 189 year: 1975 end-page: 198 ident: bib0105 article-title: Mini-mental state: a practical method for grading the cognitive state of patients for clinician publication-title: J. Psychiatr. Res. – volume: 110 start-page: 12 year: 2013 end-page: 26 ident: bib0080 article-title: Automatic detection of freezing of gait events in patients with Parkinson's disease publication-title: Comput. Methods Prog. Biomed. – volume: 63 start-page: 19 year: 2004 end-page: 24 ident: bib0045 article-title: The evolution of dysgraphia in Alzheimer's disease publication-title: Brain Res. Bull. – year: 1991 ident: bib0125 article-title: The Elements of Information Theory – volume: 14 start-page: 133 year: 2014 end-page: 138 ident: bib0090 article-title: Automatic assessing of tremor severity using nonlinear dynamics, artificial neural networks and neuro-fuzzy classifier publication-title: Adv. Electr. Comput. Eng. – volume: 37 start-page: 349 year: 2013 end-page: 357 ident: bib0010 article-title: Relearning of writing skills in Parkinson's disease: a literature review on influential factors and optimal strategies publication-title: Neurosci. Biobehav. Rev. – volume: 113 start-page: 101 year: 2014 end-page: 115 ident: bib0150 article-title: Operator functional state classification using least-square support vector machine based recursive feature elimination technique publication-title: Comput. Methods Prog. Biomed. – volume: 146 start-page: 159 year: 1997 end-page: 170 ident: bib0005 article-title: Parkinsonism reduces coordination of fingers, wrist, and arm in fine motor control publication-title: Exp. Neurol. – volume: 25 start-page: 608 year: 2006 end-page: 621 ident: bib0030 article-title: Automatic segmentation as a tool for examining the handwriting process of children with dysgraphic and proficient handwriting publication-title: Hum. Mov. Sci. – volume: 34 start-page: 4375 year: 2013 end-page: 4384 ident: bib0035 article-title: Signal-to-noise velocity peaks difference: a new method for evaluating the handwriting movement fluency in children with dysgraphia publication-title: Res. Dev. Disabil. – volume: 113 start-page: 904 year: 2014 end-page: 913 ident: bib0065 article-title: A new hybrid intelligent system for accurate detection of Parkinson's disease publication-title: Comput. Methods Prog. Biomed. – volume: 9 start-page: 1 year: 2008 end-page: 13 ident: bib0155 article-title: A comparative study of different machine learning methods on microarray gene expression data publication-title: BMC Genomics – volume: 16 start-page: 327 year: 2008 end-page: 335 ident: bib0170 article-title: Automatic classification of athletes with residual functional deficits following concussion by means of EEG signal using support vector machine publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – volume: 120 start-page: 91 year: 1997 end-page: 102 ident: bib0165 article-title: Programming of a movement sequence in Parkinson's disease publication-title: Brain – volume: 222 start-page: 230 year: 2014 end-page: 237 ident: bib0185 article-title: Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and progressive supranuclear palsy publication-title: J. Neurosci. Methods – volume: 260 start-page: 1 year: 2013 end-page: 5 ident: bib0055 article-title: Handwriting as an objective tool for Parkinson's disease diagnosis publication-title: J. Neurol. – volume: 111 start-page: 519 year: 2013 end-page: 524 ident: bib0160 article-title: Comparative evaluation of support vector machines for computer aided diagnosis of lung cancer in CT based on a multi-dimensional data set publication-title: Comput. Methods Prog. Biomed. – year: 2013 ident: bib0110 article-title: Prediction potential of different handwriting tasks for diagnosis of Parkinson's publication-title: IEEEE International Conference on e-Health and Bioengineering (EHB 2013) – year: 2009 ident: bib0140 article-title: The Elements of Statistical Learning: Data Mining, Inference, and Prediction – volume: 26 start-page: 1951 year: 2011 end-page: 1952 ident: bib0070 article-title: Acoustic assessment of voice and speech disorders in Parkinson's disease through quick vocal test publication-title: Mov. Disord. – start-page: 5151 year: 2009 end-page: 5154 ident: bib0085 article-title: deFOG: a real time system for detection and unfreezing of gait of Parkinson's patients publication-title: Engineering in Medicine and Biology Society, 2009 (EMBC 2009). Annual International Conference of the IEEE – volume: 27 start-page: 1226 year: 2005 end-page: 1239 ident: bib0130 article-title: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 33 start-page: 1301 year: 2012 end-page: 1309 ident: bib0040 article-title: Handwriting performance and underlying factors in children with attention deficit hyperactivity disorder publication-title: Res. Dev. Disabil. – volume: 197 start-page: 223 year: 2009 end-page: 233 ident: bib0020 article-title: Hypometria and bradykinesia during drawing movements in individuals with Parkinson's disease publication-title: Exp. Brain Res. – volume: 59 start-page: 1264 year: 2012 end-page: 1271 ident: bib0060 article-title: Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease publication-title: IEEE Trans. Biomed. Eng. – volume: 63 start-page: 429 year: 1997 end-page: 433 ident: bib0180 article-title: Micrographia in Parkinson's disease: the effect of providing external cues publication-title: J. Neurol. Neurosurg. Psychiatry – year: 2000 ident: bib0120 article-title: Handbook of Parametric and Nonparametric Statistical Procedures – volume: 146 start-page: 159 issue: 1 year: 1997 ident: 10.1016/j.cmpb.2014.08.007_bib0005 article-title: Parkinsonism reduces coordination of fingers, wrist, and arm in fine motor control publication-title: Exp. Neurol. doi: 10.1006/exnr.1997.6507 – volume: 110 start-page: 12 issue: 1 year: 2013 ident: 10.1016/j.cmpb.2014.08.007_bib0080 article-title: Automatic detection of freezing of gait events in patients with Parkinson's disease publication-title: Comput. Methods Prog. Biomed. doi: 10.1016/j.cmpb.2012.10.016 – volume: 260 start-page: 1 year: 2013 ident: 10.1016/j.cmpb.2014.08.007_bib0055 article-title: Handwriting as an objective tool for Parkinson's disease diagnosis publication-title: J. Neurol. doi: 10.1007/s00415-013-6996-x – volume: 26 start-page: 1951 issue: 10 year: 2011 ident: 10.1016/j.cmpb.2014.08.007_bib0070 article-title: Acoustic assessment of voice and speech disorders in Parkinson's disease through quick vocal test publication-title: Mov. Disord. doi: 10.1002/mds.23680 – volume: 9 start-page: 293 issue: 3 year: 1999 ident: 10.1016/j.cmpb.2014.08.007_bib0145 article-title: Least squares support vector machine classifiers publication-title: Neural Process. Lett. doi: 10.1023/A:1018628609742 – volume: 27 start-page: 1226 issue: 8 year: 2005 ident: 10.1016/j.cmpb.2014.08.007_bib0130 article-title: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2005.159 – year: 1991 ident: 10.1016/j.cmpb.2014.08.007_bib0125 – year: 1998 ident: 10.1016/j.cmpb.2014.08.007_bib0135 – volume: 37 start-page: 349 issue: 3 year: 2013 ident: 10.1016/j.cmpb.2014.08.007_bib0010 article-title: Relearning of writing skills in Parkinson's disease: a literature review on influential factors and optimal strategies publication-title: Neurosci. Biobehav. Rev. doi: 10.1016/j.neubiorev.2013.01.015 – volume: 63 start-page: 429 year: 1997 ident: 10.1016/j.cmpb.2014.08.007_bib0180 article-title: Micrographia in Parkinson's disease: the effect of providing external cues publication-title: J. Neurol. Neurosurg. Psychiatry doi: 10.1136/jnnp.63.4.429 – volume: 4 start-page: 195 issue: 2 year: 2012 ident: 10.1016/j.cmpb.2014.08.007_bib0050 article-title: An information analysis of in-air and on-surface trajectories in online handwriting publication-title: Cogn. Comput. doi: 10.1007/s12559-011-9119-y – volume: 59 start-page: 1264 issue: 5 year: 2012 ident: 10.1016/j.cmpb.2014.08.007_bib0060 article-title: Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2012.2183367 – year: 2000 ident: 10.1016/j.cmpb.2014.08.007_bib0120 – volume: 12 start-page: 189 year: 1975 ident: 10.1016/j.cmpb.2014.08.007_bib0105 article-title: Mini-mental state: a practical method for grading the cognitive state of patients for clinician publication-title: J. Psychiatr. Res. – year: 2013 ident: 10.1016/j.cmpb.2014.08.007_bib0110 article-title: Prediction potential of different handwriting tasks for diagnosis of Parkinson's – volume: 222 start-page: 230 year: 2014 ident: 10.1016/j.cmpb.2014.08.007_bib0185 article-title: Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and progressive supranuclear palsy publication-title: J. Neurosci. Methods doi: 10.1016/j.jneumeth.2013.11.016 – volume: 34 start-page: 4375 issue: 12 year: 2013 ident: 10.1016/j.cmpb.2014.08.007_bib0035 article-title: Signal-to-noise velocity peaks difference: a new method for evaluating the handwriting movement fluency in children with dysgraphia publication-title: Res. Dev. Disabil. doi: 10.1016/j.ridd.2013.09.012 – start-page: 153 year: 1987 ident: 10.1016/j.cmpb.2014.08.007_bib0095 article-title: Unified Parkinson's disease rating scale – start-page: 842 issue: 8 year: 2011 ident: 10.1016/j.cmpb.2014.08.007_bib0115 article-title: Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity publication-title: J. R. Soc. Interface doi: 10.1098/rsif.2010.0456 – volume: 9 start-page: 1 issue: Suppl. 1 year: 2008 ident: 10.1016/j.cmpb.2014.08.007_bib0155 article-title: A comparative study of different machine learning methods on microarray gene expression data publication-title: BMC Genomics – volume: 120 start-page: 91 year: 1997 ident: 10.1016/j.cmpb.2014.08.007_bib0165 article-title: Programming of a movement sequence in Parkinson's disease publication-title: Brain doi: 10.1093/brain/120.1.91 – volume: 33 start-page: 1301 issue: 4 year: 2012 ident: 10.1016/j.cmpb.2014.08.007_bib0040 article-title: Handwriting performance and underlying factors in children with attention deficit hyperactivity disorder publication-title: Res. Dev. Disabil. doi: 10.1016/j.ridd.2012.02.010 – volume: 14 start-page: 133 issue: 1 year: 2014 ident: 10.1016/j.cmpb.2014.08.007_bib0090 article-title: Automatic assessing of tremor severity using nonlinear dynamics, artificial neural networks and neuro-fuzzy classifier publication-title: Adv. Electr. Comput. Eng. doi: 10.4316/AECE.2014.01020 – volume: 197 start-page: 223 issue: 3 year: 2009 ident: 10.1016/j.cmpb.2014.08.007_bib0020 article-title: Hypometria and bradykinesia during drawing movements in individuals with Parkinson's disease publication-title: Exp. Brain Res. doi: 10.1007/s00221-009-1925-z – volume: 14 start-page: 436 issue: 2 year: 2010 ident: 10.1016/j.cmpb.2014.08.007_bib0075 article-title: Wearable assistant for Parkinson's disease patients with the freezing of gait symptom publication-title: IEEE Trans. Inf. Technol. Biomed. doi: 10.1109/TITB.2009.2036165 – volume: 63 start-page: 19 issue: 1 year: 2004 ident: 10.1016/j.cmpb.2014.08.007_bib0045 article-title: The evolution of dysgraphia in Alzheimer's disease publication-title: Brain Res. Bull. doi: 10.1016/j.brainresbull.2003.11.005 – volume: 113 start-page: 101 issue: 1 year: 2014 ident: 10.1016/j.cmpb.2014.08.007_bib0150 article-title: Operator functional state classification using least-square support vector machine based recursive feature elimination technique publication-title: Comput. Methods Prog. Biomed. doi: 10.1016/j.cmpb.2013.09.007 – volume: 113 start-page: 904 issue: 3 year: 2014 ident: 10.1016/j.cmpb.2014.08.007_bib0065 article-title: A new hybrid intelligent system for accurate detection of Parkinson's disease publication-title: Comput. Methods Prog. Biomed. doi: 10.1016/j.cmpb.2014.01.004 – volume: 25 start-page: 2649 issue: 15 year: 2010 ident: 10.1016/j.cmpb.2014.08.007_bib0100 article-title: Systematic review of levodopa dose equivalency reporting in Parkinson's disease publication-title: Mov. Disord. doi: 10.1002/mds.23429 – volume: 113 start-page: 609 issue: 5 year: 2006 ident: 10.1016/j.cmpb.2014.08.007_bib0025 article-title: Kinematic analysis of dopaminergic effects on skilled handwriting movements in Parkinson's disease publication-title: J. Neural Transm. doi: 10.1007/s00702-005-0346-9 – volume: 16 start-page: 327 issue: 4 year: 2008 ident: 10.1016/j.cmpb.2014.08.007_bib0170 article-title: Automatic classification of athletes with residual functional deficits following concussion by means of EEG signal using support vector machine publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2008.918422 – volume: 11 start-page: 289 issue: 3 year: 1996 ident: 10.1016/j.cmpb.2014.08.007_bib0015 article-title: Computational analysis of open loop handwriting movements in Parkinson's disease: a rapid method to detect dopamimetic effects publication-title: Mov. Disord. doi: 10.1002/mds.870110313 – start-page: 5151 year: 2009 ident: 10.1016/j.cmpb.2014.08.007_bib0085 article-title: deFOG: a real time system for detection and unfreezing of gait of Parkinson's patients – volume: 25 start-page: 608 year: 2006 ident: 10.1016/j.cmpb.2014.08.007_bib0030 article-title: Automatic segmentation as a tool for examining the handwriting process of children with dysgraphic and proficient handwriting publication-title: Hum. Mov. Sci. doi: 10.1016/j.humov.2006.07.005 – year: 2009 ident: 10.1016/j.cmpb.2014.08.007_bib0140 – volume: 111 start-page: 519 issue: 2 year: 2013 ident: 10.1016/j.cmpb.2014.08.007_bib0160 article-title: Comparative evaluation of support vector machines for computer aided diagnosis of lung cancer in CT based on a multi-dimensional data set publication-title: Comput. Methods Prog. Biomed. doi: 10.1016/j.cmpb.2013.04.016 – volume: 116 start-page: 123 issue: 2 year: 2014 ident: 10.1016/j.cmpb.2014.08.007_bib0175 article-title: Auto-adaptive robot-aided therapy using machine learning techniques publication-title: Comput. Methods Prog. Biomed. doi: 10.1016/j.cmpb.2013.09.011 |
| SSID | ssj0002556 |
| Score | 2.4147866 |
| Snippet | •We exploit the fact that movement during handwriting of a text consists of on-surface and in-air movements and use it for differential diagnosis of PD.•By... Highlights • We exploit the fact that movement during handwriting of a text consists of on-surface and in-air movements and use it for differential diagnosis... Parkinson's disease (PD) is the second most common neurodegenerative disease affecting significant portion of elderly population. One of the most frequent... Background and objective: Parkinson's disease (PD) is the second most common neurodegenerative disease affecting significant portion of elderly population. One... |
| SourceID | proquest pubmed pascalfrancis crossref elsevier |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 405 |
| SubjectTerms | Accuracy Aged Algorithms Applied sciences Artificial Intelligence Biological and medical sciences Biomechanical Phenomena Case-Control Studies Classifications Computer science; control theory; systems Computerized, statistical medical data processing and models in biomedicine Control equipment Data processing. List processing. Character string processing Decision support systems Decision Support Systems, Clinical Diagnosis Diagnosis, Differential Disease classification Exact sciences and technology Female Hand - physiology Handwriting Headache. Facial pains. Syncopes. Epilepsia. Intracranial hypertension. Brain oedema. Cerebral palsy Humans In-air movement Internal Medicine Male Markers Medical sciences Memory organisation. Data processing Micrographia Middle Aged Models and simulation Motor Skills Movement Nervous system (semeiology, syndromes) Neurology Other Parkinson Disease - diagnosis Parkinson Disease - physiopathology Parkinson's disease Patients Pattern recognition. Digital image processing. Computational geometry Reproducibility of Results Software Support Vector Machine |
| Title | Analysis of in-air movement in handwriting: A novel marker for Parkinson's disease |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S0169260714003204 https://www.clinicalkey.es/playcontent/1-s2.0-S0169260714003204 https://dx.doi.org/10.1016/j.cmpb.2014.08.007 https://www.ncbi.nlm.nih.gov/pubmed/25261003 https://www.proquest.com/docview/1629960886 https://www.proquest.com/docview/1635042780 https://www.proquest.com/docview/1651430033 |
| Volume | 117 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3da9swEBelhTEYo_v21gUNBnsYXmxLsqS-hbKSbbQP2wp9E5IslYzGCXFK3_q3TyfLKWVdBnsx2Dph6Xw6nXx3v0PovTC-0sIXeSW0yYPCK3PNpc8bzcCg5cRHj-7JaT09o1_P2fkOOhpyYSCsMun-XqdHbZ2ejBM3x8vZbPwDcEQqgEcLYkiqiAlKKQcp_3RzG-YBEFs9vrfMgTolzvQxXna-NBDeRSOMJ5SUvX9zerTUXWCZ72td_N0YjZvS8T56nKxJPOkH_ATtuPYpenCS_OXP0PcBcwQvPJ61uZ6t8HwRIcLX4R7Db_NrgDVqLw7xBLeh6RLPIWJnhYM1iyEnOqaHfehwcuU8R2fHn38eTfNURSG3jIl1LsMGVGrmeEVsI01JdelKU7i68gX13EonpOaNN14K7nQhbVMHI6zR1pbUC0deoN120bpXCBNJtSXEhCunxtRQVF0LLrwlTBNOMlQO7FM2QYxDpYtLNcSS_VLAcgUsV1D-suAZ-rjps-wBNrZSk-GrqCF1NCg7FfT_1l78vl6uS-u1U6XqKlWoP2QqQ2zT845Y_vONozsis5laON_CoVBm6N0gQyosaPDS6NYtrsJQ6goQc4Sot9EQFoukFNtowBaGWn0ZetkL6e0oWDg4h6bX_zm9N-gh3PVhPQdod726cm-DcbY2o7j6Rmhv8uXb9PQ3skc2hA |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3da9swED9KCtugjH3P--g0GOxhmNiWZUl7C2UlXZs8bC30TUiyNDIaJyQp_fens-WMsi6DvRhs6bB0Pp1OvrvfAXwQxhda-CwthDZpUHh5qrn0aa0ZGrSc-tajO5lW44vy6yW73IOjPhcGwyqj7u90equt45Nh5OZwOZsNvyOOSIHwaEEMaYGYoPuITsUGsD86OR1PtwoZUbY6iG-ZIkHMnenCvOx8aTDCq2yRPLGq7N3708FSrwPXfFfu4u_2aLsvHT-Ch9GgJKNuzI9hzzVP4N4kusyfwrcedoQsPJk1qZ6tyHzRooRvwj3BP-c3iGzU_PhMRqQJTVdkjkE7KxIMWoJp0W2G2Mc1id6cZ3Bx_OX8aJzGQgqpZUxsUhn2oFwzxwtqa2nyUucuN5mrCp-VnlvphNS89sZLwZ3OpK2rYIfV2tq89MLR5zBoFo17CYTKUltKTbjy0pgK66prwYW3lGnKaQJ5zz5lI8o4Fru4Un042U-FLFfIcoUVMDOewKctzbLD2NjZm_ZfRfXZo0HfqbAF7KTid1G5dVyya5WrdaEy9YdYJcC2lLck859vPLwlMtuphSMungtlAu97GVJhTaOjRjducR2GUhUImiNEtasPZW2dlGxXHzSHsVxfAi86If09ChbOzqHp1X9O7x3cH59PztTZyfT0NTzAli7K5w0MNqtr9zbYahtzGNfiLz0mOTU |
| 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=Analysis+of+in-air+movement+in+handwriting%3A+A+novel+marker+for+Parkinson%27s+disease&rft.jtitle=Computer+methods+and+programs+in+biomedicine&rft.au=Drotar%2C+Peter&rft.au=Mekyska%2C+Jiri&rft.au=Rektorova%2C+Irena&rft.au=Masarova%2C+Lucia&rft.date=2014-12-01&rft.issn=0169-2607&rft.volume=117&rft.issue=3&rft.spage=405&rft.epage=411&rft_id=info:doi/10.1016%2Fj.cmpb.2014.08.007&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_m | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fcdn.clinicalkey.com%2Fck-thumbnails%2F01692607%2FS0169260714X00155%2Fcov150h.gif |