Analysis of on-surface and in-air movement in handwriting of subjects with Parkinson's disease and atypical parkinsonism.
Parkinson’s disease
handwriting
multi-class linear discriminant analysis
on-surface and in-air kinematic parameters
Journal
Biomedizinische Technik. Biomedical engineering
ISSN: 1862-278X
Titre abrégé: Biomed Tech (Berl)
Pays: Germany
ID NLM: 1262533
Informations de publication
Date de publication:
24 Apr 2019
24 Apr 2019
Historique:
received:
19
08
2017
accepted:
22
03
2018
pubmed:
1
5
2018
medline:
16
10
2019
entrez:
1
5
2018
Statut:
ppublish
Résumé
The purpose of this paper is to emphasize the importance of in-air movement besides on-surface movement for handwriting analysis. The proposed method uses a classification of drawing healthy subjects and subjects with Parkinson's disease, according to their on-surface and in-air handwriting parameters during their writing on a graphical tablet. Experimental results on real data sets demonstrate that the highest accuracy of subject's classification was obtained by combining both on-surface and in-air kinematic parameters.
Identifiants
pubmed: 29708872
doi: 10.1515/bmt-2017-0148
pii: bmt-2017-0148
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
187-194Références
Teulings HL, Conreras-Vidal JL, Stelmach GE, Adler CH. Parkisonism reduces coordination of fingers, wrist and arm in fine motor control. Exp Neurol 1997;146:159–70.
Marsden CD. Slowness of movement in Parkison’s disease. Mov Disord 1989;4:s23–35.
Boisseau M, Chamberland G, Gauthier S. Handwriting analysis of several extrapyramidal disorders, The meeting of the CSFS, Niagara Falls, Canada, September 1986.
Teulings HL, Stelmach GE. Control of stroke size, peak acceleration, and stroke duration in Parkinsonian handwriting. Hum Mov Sci 1991;10:315–33.
Tucha O, Mecklinger L, Thome J, Reiter A, Alders GL, Sartor H, et al. Kinematic analysis of dopaminergic effects on skilled handwriting movements in Parkinson’s disease. J Neural Transm 2006;113:606–23.
Rosenblum S, Samuel M, Zlotnik S, Erikh I, Schlesinger H. Handwriting as an objective tool for Parkinson’s disease diagnosis. J Neurol 2013;260:2357–61.
Drotar P, Mekyska J, Rektorova I, Masarova L, Smekal Z, Faundez-Zanuy M. Analysis of in-air movement in handwriting: a novel marker for Parkinson’s disease. Comput Methods Programs Biomed 2013;117:405–11.
Miler Jerkovic V, Kojic V, Popovic MB. An information and reliability analysis of handwriting kinematics. 2nd IcEtran Conference, Silver Lake, Serbia, 2015, MEI1.2.1–5.
Hughes AJ, Daniel SE, Kilford L, Lees AJ. Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: a clinico-pathological study of 100 cases. J Neurol Neurosurg Psychiatry 1992;55:181–4.
Litvan I, Hauw JJ, Bartko JJ, Lantos PL, Daniel SE, Horoupian DS, et al. Validity and reliability of the preliminary ninds neuropathologic criteria for progressive supranuclear palsy and related disorders. J Neuropathol Exp Neurol 1996;55:97–105.
Gilman S, Wenning GK, Low PA, Brooks DJ, Mathias CJ, Trojanowski JQ, et al. Second consensus statement on the diagnosis of multiple system atrophy. Neurology 2008;71:670–6.
Teulings HL, Stemlach GE. Signal-to-noise ratio of handwriting size, force and time: Cues to early markers of Parkinson’s disease? Proceedings of the NATO advanced Research Workshop on Sensorimotor Impairment in the Elderly, Bad Windsheim, Germany, September 11–13, 1992:311–28. ISBN 978-94-010-4872–9.
Accardo AA, Genna M, Borean M. Development, maturation and learning influence on handwriting kinematics. Hum Mov Sci 2013;32:136–46.
Marquardt C, Mai N. A computational procedure for movement analysis in handwriting. J Neurosci Methods 1994;52:39–54.
Teulings HL, Maarse FJ. Digital recording and processing of handwriting movements. Hum Mov Sci 1984;3:193–217.
Eichhorn TE, Gasser T, Mai N, Marquardt C, Arnold G, Schwarz J, et al. Computational analysis of open loop handwriting movements in Parkinson’s disease: a rapid method to detect dopamimetic effects. Mov Disord 1996;11:289–97.
Schröter A, Mergl R, Bürger K, Hampel H, Möller HJ, Hegerl U. Kinematic analysis of handwriting movements in patients with Alzheimer’s disease, mild cognitive impairment, depression and healthy subjects. Dement Geriatr Cogn Disord 2003;15:132–42.
Cohen J. Statistical Power Analysis for the Behavioral Science. New York: Academic Press; 1977.
Cover TM, Thomas JA. Entropy, Relative Entropy and Mutual Information, Elements of Information Theory. New Jersey: John Wiley & Sons; 2006:13–54.
Elliot AC, Hynan LS. A SAS macro implementation of a multiple comparison post hoc test for a Kruskal-Wallis analysis. Comput Methods Programs Biomed 2011;102:75–80.
Duda RO, Hart PE, Stork DG. Pattern Classification. New York: John Wiley & Sons; 2000.
Fukunaga K. Introduction to statistical pattern recognition. New York: Academic Press; 1994.
Miler Jerkovic V, Malesevic B. Block representation of generalized inverses of matrices, Proceedings of the 5th Symposium “Mathematics and applications”, Faculty of Mathematics, University of Belgrade and Serbian Academy of Sciences and Arts, 2014;1:176–85.
Sokolova M, Lapalme A. A systematic analysis of performance measures for classification tasks. Inf Process Manag 2009;45:427–37.
Sesa-Nogueras E, Faundez-Zanuy M, Mekyska J. An information analysis of in-air and on-surface trajectories in online handwriting. Cogn Comput 2012;4:195–205.
Miler Jerkovic V, Kojic V, Popovic MB. Linear Discriminant Analysis: Classification of On-Surface and In-Air Handwriting, 23rd Telecommunications forum TELFOR 2015, Belgrade, Serbia, 2015:460–3.
Tsanas A, Little P, McSharry P, Spielman J, Raming L. Novel speech signal processing algorithms for high-accuracy classification of Parkinson’s disease. IEEE Trans Biomed Eng 2012;59:1264–71.