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Handwriting Analysis to Support Alzheimer’s Disease Diagnosis: A Preliminary Study

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11679)

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.

Keywords

Handwriting Classification algorithm Alzheimer’s disease 
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References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    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_23CrossRefGoogle Scholar
  5. 5.
    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_5CrossRefGoogle Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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) CrossRefGoogle Scholar
  8. 8.
    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.2018.2840679CrossRefGoogle Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    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)Google Scholar
  15. 15.
    Price, D.L.: Aging of the brain and dementia of the Alzheimer type. Princ. Neural Sci., 1149–1168 (2000)Google Scholar
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    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)CrossRefGoogle Scholar
  18. 18.
    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)CrossRefGoogle Scholar
  19. 19.
    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)CrossRefGoogle Scholar
  20. 20.
    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)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electrical and Information Engineering (DIEI)University of Cassino and Southern LazioCassinoItaly

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