Abstract
Alzheimer’s disease (AD) is the most common neurodegenerative dementia of old age and the leading chronic disease contributor to disability and dependence among older people worldwide. Handwriting is among the motor activities compromised by AD, which is the result of a complex network of cognitive, kinaesthetic and perceptive-motor skills. Indeed, researchers have shown that the patients affected by these diseases exhibit alterations in the spatial organization and poor control of movement. In this paper, we present the preliminary results of a study in which an experimental protocol (including the copy of words, letters and sentence task) has been used to assess the kinematic properties of the movements involved in the handwriting. The obtained results are very encouraging and seem to confirm the hypothesis that machine learning-based analysis of handwriting can be profitably used to support AD diagnosis.
This work is supported by the Italian Ministry of Education, University and Research (MIUR) within the PRIN2015-HAND project.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Babiloni, C., et al.: Classification of single normal and Alzheimer’s disease individuals from cortical sources of resting state EEG rhythms. Front. Neurosci. 10, 47 (2016)
Bevilacqua, V., D’Ambruoso, D., Mandolino, G., Suma, M.: A new tool to support diagnosis of neurological disorders by means of facial expressions. In: Proceedings of the 2011 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2011 (2011)
Cilia, N., De Stefano, C., Fontanella, F., Scotto di Freca, A.: An experimental protocol to support cognitive impairment diagnosis by using handwriting analysis. Procedia Comput. Sci. 141, 466–471 (2018)
Cordella, L.P., De Stefano, C., Fontanella, F., Scotto di Freca, A.: A weighted majority vote strategy using Bayesian networks. In: Petrosino, A. (ed.) ICIAP 2013. LNCS, vol. 8157, pp. 219–228. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41184-7_23
De Stefano, C., Fontanella, F., Folino, G., di Freca, A.S.: A Bayesian approach for combining ensembles of GP classifiers. In: Sansone, C., Kittler, J., Roli, F. (eds.) MCS 2011. LNCS, vol. 6713, pp. 26–35. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21557-5_5
De Stefano, C., Fontanella, F., Impedovo, D., Pirlo, G., Scotto di Freca, A.: Handwriting analysis to support neurodegenerative diseases diagnosis: a review. Pattern Recognit. Lett. 121, 37–45 (2018)
Hayashi, A., et al.: Neural substrates for writing impairments in japanese patients with mild Alzheimer’s disease: a spect study. Neuropsychologia 49(7), 1962–1968 (2011)
Impedovo, D., Pirlo, G.: Dynamic handwriting analysis for the assessment of neurodegenerative diseases: a pattern recognition perspective. IEEE Rev. Biomed. Eng. 12, 209–220 (2019). https://doi.org/10.1109/RBME.2018.2840679
Jelic, V., Dierks, T., Amberla, K., Almkvist, O., Winblad, B., Nordberg, A., Tsukahara, N.: Longitudinal changes in quantitative EEG during long-term tacrine treatment of patients with Alzheimer’s disease. Neurosci. Lett. 254(4), 85–88 (1988)
Kang, J., Lemaire, H.G., Unterbeck, A., Salbaum, J.M., Masters, C.L., Grzeschik, K.H., et al.: The precursor of Alzheimer’s disease amyloid A4 protein resembles a cell-surface receptor. Nature 325, 733–736 (2015)
Lambert, J., Giffard, B., Nore, F., de la Sayette, V., Pasquier, F., Eustache, F.: Central and peripheral agraphia in Alzheimer’s disease: From the case of auguste D. to a cognitive neuropsychology approach. Cortex 43(7), 935–951 (2007)
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., Stadlan, E.: Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA work group under the auspices of department of health and human services task force on Alzheimer’s disease. Neurology 34, 939–944 (1984)
Onofri, E., Mercuri, M., Archer, T., Ricciardi, M.R., Massoni, F., Ricci, S.: Effect of cognitive fluctuation on handwriting in Alzheimer’s patient: a case study. Acta Medica Mediterranea 3, 751 (2015)
Onofri, E., Mercuri, M., Salesi, M., Ricciardi, M., Archer, T.: Dysgraphia in relation to cognitive performance in patients with Alzheimer’s disease. J. Intellect. Disabil. Diagn. Treat. 1, 113–124 (2013)
Price, D.L.: Aging of the brain and dementia of the Alzheimer type. Princ. Neural Sci., 1149–1168 (2000)
Slavin, M.J., Phillips, J.G., Bradshaw, J.L., Hall, K.A., Presnell, I.: Consistency of handwriting movements in dementia of the Alzheimer’s type: a comparison with Huntington’s and Parkinson’s diseases. J. Int. Neuropsychol. Soc. 5(1), 20–25 (1999)
Triggiani, A., et al.: Classification of healthy subjects and Alzheimer’s disease patients with dementia from cortical sources of resting state EEG rhythms: a study using artificial neural networks. Front. Neurosci. 10, 604 (2017)
Tseng, M.H., Cermak, S.A.: The influence of ergonomic factors and perceptual-motor abilities on handwriting performance. Am. J. Occup. Ther. 47(10), 919–926 (1993)
Werner, P., Rosenblum, S., Bar-On, G., Heinik, J., Korczyn, A.: Handwriting process variables discriminating mild Alzheimer’s disease and mild cognitive impairment. J. Gerontol. Psychol. Sci. 61(4), 228–236 (2006)
Yan, J.H., Rountree, S., Massman, P., Doody, R.S., Li, H.: Alzheimer’s disease and mild cognitive impairment deteriorate fine movement control. J. Psychiatr. Res. 42(14), 1203–1212 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Cilia, N.D., De Stefano, C., Fontanella, F., Molinara, M., Scotto Di Freca, A. (2019). Handwriting Analysis to Support Alzheimer’s Disease Diagnosis: A Preliminary Study. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11679. Springer, Cham. https://doi.org/10.1007/978-3-030-29891-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-29891-3_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29890-6
Online ISBN: 978-3-030-29891-3
eBook Packages: Computer ScienceComputer Science (R0)