Abstract
In this paper, we propose a novel model-free technique for differentiating both Parkinson’s Disease (PD) patients from healthy subjects and mild PD patients from moderate ones by using a handwriting analysis tool. The tool is based on the analysis of biometric signals and the application of Artificial Neural Network (ANN)-based classifier. Experimental tests have been carried on with both healthy and PD subjects to identify the most representative features and to assess the accuracy and repeatability of classification performances achieved through optimal topology ANNs. Finally, the obtained results are reported and discussed to infer some important properties on classification approaches and the role of muscular activities on the handwriting analysis applied to neurodegenerative disease research.
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
Bibliography
Carmeli, E., Patish, H., Coleman, R.: The aging hand. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 58, M146–M152 (2003)
Van Gemmert, A.W.A., Teulings, H.-L., Contreras-Vidal, J.L., Stelmach, G.E.: Parkinsons disease and the control of size and speed in handwriting. Neuropsychologia 37, 685–694 (1999)
Drotar, P., Mekyska, J., Smekal, Z., Rektorova, I., Masarova, L., Faundez-Zanuy, M.: Prediction potential of different handwriting tasks for diagnosis of Parkinson’s. In: 2013 E-Health and Bioengineering Conference (EHB), pp. 1–4 (2013)
Rosenblum, S., Samuel, M., Zlotnik, S., Erikh, I., Schlesinger, I.: Handwriting as an objective tool for Parkinson’s disease diagnosis. J. Neurol. 260, 2357–2361 (2013)
Loconsole, C., et al.: Computer vision and EMG-based handwriting analysis for classification in Parkinson’s disease. In: Huang, D.-S., Jo, K.-H., Figueroa-García, J.C. (eds.) ICIC 2017. LNCS, vol. 10362, pp. 493–503. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-63312-1_43
Loconsole, C., Cascarano, G.D., Brunetti, A., Trotta, G.F., Losavio, G., Bevilacqua, V., Di Sciascio, E.: A model-free technique based on computer vision and sEMG for classification in Parkinson’s disease by using computer-assisted handwriting analysis. Pattern Recognit. Lett. (2018, in press). https://doi.org/10.1016/j.patrec.2018.04.006
Bevilacqua, V., Brunetti, A., Triggiani, M., Magaletti, D., Telegrafo, M., Moschetta, M.: An optimized feed-forward artificial neural network topology to support radiologists in breast lesions classification. In: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion - GECCO 2016 Companion, pp. 1385–1392 (2016)
Acknowledgments
This work has been partially funded from the FutureInResearch program of the Regione Puglia - project n. JTFWZV0 ABIOSAN - Advanced BIOmetric analysiS Against Neuromuscular disease.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Bevilacqua, V. et al. (2018). A Model-Free Computer-Assisted Handwriting Analysis Exploiting Optimal Topology ANNs on Biometric Signals in Parkinson’s Disease Research. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_74
Download citation
DOI: https://doi.org/10.1007/978-3-319-95933-7_74
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95932-0
Online ISBN: 978-3-319-95933-7
eBook Packages: Computer ScienceComputer Science (R0)