Abstract
Parkinson’s disease affects millions of people worldwide. Nowadays there are several ways to help diagnose this disease. Among which we can highlight handwriting exams. One of the main contributions of the computational field to help diagnose this disease is the feature extraction of handwriting exams. This paper proposed a similarity extraction approach which was applied to the exam template and the handwritten trace of the patient. The similarity metrics used in this work were: structural similarity, mean squared error and peak signal-to-noise ratio. The proposed approach was evaluated with variations in obtaining the exam template and the handwritten trace generated by the patient. Each of these variations was used together with the Nave Bayes, OPF, and SVM classifiers. In conclusion, the proposed approach demonstrated that it was better than the other approach found in the literature, and is therefore a potential aid in the detection and monitoring of Parkinson’s disease.
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References
Bouadjenek, N., Nemmour, H., Chibani, Y.: Robust soft-biometrics prediction from off-line handwriting analysis. Appl. Soft Comput. 46, 980–990 (2016)
Drotár, P., Mekyska, J., Rektorová, I., Masarová, L., Smékal, Z., Faundez-Zanuy, M.: Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson’s disease. Artif. Intell. Med. 67, 39–46 (2016)
Drotár, P., Mekyska, J., Smékal, Z., Rektorová, I., Masarová, L., Faundez-Zanuy, M.: Contribution of different handwriting modalities to differential diagnosis of Parkinson’s disease. In: 2015 IEEE International Symposium on Medical Measurements and Applications, pp. 344–348 (2015)
Graça, R., e Castro, R.S., Cevada, J.: Parkdetect: Early diagnosing Parkinson’s disease. In: 2014 IEEE International Symposium on Medical Measurements and Applications, pp. 1–6 (2014)
Huynh-Thu, Q., Ghanbari, M.: Scope of validity of PSNR in image/video quality assessment. Electron. Lett. 44(13), 800–801 (2008)
de Ipiña, K.L., Iturrate, M., Calvo, P.M., Beitia, B., Garcia-Melero, J., Bergareche, A., De la Riva, P., Marti-Masso, J.F., Faundez-Zanuy, M., Sesa-Nogueras, E., Roure, J., Solé-Casals, J.: Selection of entropy based features for the analysis of the archimedes’ spiral applied to essential tremor. In: 2015 4th International Work Conference on Bioinspired Intelligence (IWOBI), pp. 157–162 (2015)
Papa, J.P., Falcão, A.X., Suzuki, C.T.N.: Supervised pattern classification based on optimum-path forest. Int. J. Imaging Syst. Technol. 19(2), 120–131 (2009)
Parkinson, J.: An Essay on the Shaking Palsy. Whittingham and Rowland, London (1817)
Pereira, C.R., Pereira, D.R., da Silva, F.A., Hook, C., Weber, S.A.T., Pereira, L.A.M., Papa, J.P.: A step towards the automated diagnosis of Parkinson’s disease: analyzing handwriting movements. In: 2015 IEEE 28th International Symposium on Computer-Based Medical Systems, pp. 171–176 (2015)
Pereira, C.R., Pereira, D.R., Silva, F.A., Masieiro, J.P., Weber, S.A.T., Hook, C., Papa, J.P.: A new computer vision-based approach to aid the diagnosis of Parkinson’s disease. Comput. Methods Programs Biomed. 136, 79–88 (2016)
Rebouças Filho, P.P., Moreira, F.D.L., de Lima Xavier, F.G., Gomes, S.L., dos Santos, J.C., Freitas, F.N.C., Freitas, R.G.: New analysis method application in metallographic images through the construction of mosaics via speeded up robust features and scale invariant feature transform. Materials 8(7), 3864–3882 (2015)
Schiffer, A., Nevado-Holgado, A.J., Johnen, A., Schönberger, A.R., Fink, G.R., Schubotz, R.I.: Intact action segmentation in Parkinson’s disease: hypothesis testing using a novel computational approach. Neuropsychologia 78, 29–40 (2015)
Sengoku, R., Matsushima, S., Bono, K., Sakuta, K., Yamazaki, M., Miyagawa, S., Komatsu, T., Mitsumura, H., Kono, Y., Kamiyama, T., Ito, K., Mochio, S., Iguchi, Y.: Olfactory function combined with morphology distinguishes Parkinson’s disease. Parkinsonism & Relat. Disord. 21(7), 771–777 (2015)
Shah, V.V., Goyal, S., Palanthandalam-Madapusi, H.J.: A perspective on the use of high-frequency stimulation in deep brain stimulation for Parkinson’s disease. In: 2016 Indian Control Conference (ICC), pp. 19–24 (2016)
Surangsrirat, D., Intarapanich, A., Thanawattano, C., Bhidayasiri, R., Petchrutchatachart, S., Anan, C.: Tremor assessment using spiral analysis in time-frequency domain. In: 2013 Proceedings of IEEE Southeastcon, pp. 1–6, April 2013
Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic Press, New York (2009)
Ünlü, A., Brause, R., Krakow, K.: Handwriting Analysis for Diagnosis and Prognosis of Parkinson’s Disease, pp. 441–450. Springer, Heidelberg (2006)
Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (2009)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
WHO: Neurological Disorders: Public Health Challenges. World Health Organization (2006)
WHO: Mental health and older adults, April 2016. http://www.who.int/mediacentre/factsheets/fs381/en/
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de Souza, J.W.M., Almeida, J.S., Rebouças Filho, P.P. (2018). A New Approach for the Diagnosis of Parkinson’s Disease Using a Similarity Feature Extractor. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2017. Advances in Intelligent Systems and Computing, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-76348-4_3
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