Abstract
Timely diagnosis plays a crucial role for the treatment of neurodegenerative diseases. In particular, Dementia Identification in early stages is important to help patients have a better quality of life and to help clinicians to find a pathway of treatments to slow the effects. To the aim, a wide set of different handwriting tasks is here considered, and Shallow and Deep Learning methodologies are compared. Furthermore, Random Hybrid Stroke (RHS) are adopted to represent the handwriting time series. This solution outperforms the classical Deep Learning methodology and it is compared to a state-of-art shallow learning approach. Finally, a decision-level fusion for the results is adopted.
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References
Impedovo, D., Pirlo, G.: Dynamic handwriting analysis for the assessment of neurodegenerative diseases: a pattern recognition perspective. IEEE Rev. Biomed. Eng. 12, 209–220 (2018). https://doi.org/10.1109/RBME.2018.2840679
de Stefano, C., Fontanella, F., Impedovo, D., Pirlo, G., Scotto di Freca, A.: Handwriting analysis to support neurodegenerative diseases diagnosis: a review. Pattern Recogn. Lett. 121, 37–45 (2019). https://doi.org/10.1016/J.PATREC.2018.05.013
Ström, F., Koker, R.: A parallel neural network approach to prediction of Parkinson’s disease. Expert Syst. Appl. 38, 12470–12474 (2011). https://doi.org/10.1016/J.ESWA.2011.04.028
Faundez-Zanuy, M., Mekyska, J., Impedovo, D.: Online handwriting, signature and touch dynamics: tasks and potential applications in the field of security and health. Cogn. Comput. 13, 1406–1421 (2021). https://doi.org/10.1007/S12559-021-09938-2/TABLES/1
Impedovo, D., Pirlo, G., Vessio, G., Angelillo, M.T.: A Handwriting-based protocol for assessing neurodegenerative dementia. Cogn. Comput. 11(4), 576–586 (2019). https://doi.org/10.1007/s12559-019-09642-2
Cilia, N.D., De Stefano, C., Fontanella, F., Molinara, M., Scotto Di Freca, A.: Using handwriting features to characterize cognitive impairment. In: Ricci, E., Rota Bulò, S., Snoek, C., Lanz, O., Messelodi, S., Sebe, N. (eds.) Image Analysis and Processing – ICIAP 2019. LNCS, vol. 11752, pp. 683–693. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30645-8_62
Kahindo, C., El-Yacoubi, M.A., Garcia-Salicetti, S., Rigaud, A.-S., Cristancho-Lacroix, V.: Characterizing early-stage alzheimer through spatiotemporal dynamics of handwriting; characterizing early-stage alzheimer through spatiotemporal dynamics of handwriting. IEEE Sig. Process. Lett. 25 (2018). https://doi.org/10.1109/LSP.2018.2794500
Cilia, N.D.: Handwriting analysis to support Alzheimer disease diagnosis: a preliminary study. In: Vento, M., Percannella, G. (eds.) Computer Analysis of Images and Patterns, vol. 11679, pp. 143–151. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29891-3_13
Cilia, N.D., de Stefano, C., Fontanella, F., di Freca, A.S.: An experimental protocol to support cognitive impairment diagnosis by using handwriting analysis. Procedia Comput. Sci. 141, 466–471 (2018). https://doi.org/10.1016/J.PROCS.2018.10.141
Dentamaro, V., Impedovo, D., Pirlo, G.: An analysis of tasks and features for neuro-degenerative disease assessment by handwriting. In: Del Bimbo, A., et al. (eds.) ICPR 2021. LNCS, vol. 12661, pp. 536–545. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68763-2_41
Dentamaro, V., Giglio, P., Impedovo, D., Pirlo, G.: Benchmarking of shallow learning and deep learning techniques with transfer learning for neurodegenerative disease assessment through handwriting. In: Barney Smith, E.H., Pal, U. (eds.) ICDAR 2021. LNCS, vol. 12917, pp. 7–20. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86159-9_1
Drotár, P., Mekyska, J., Rektorová, I., Masarová, L., Smékal, Z., Faundez-Zanuy, M.: Decision support framework for Parkinson’s disease based on novel handwriting markers. IEEE Trans. Neural Syst. Rehabil. Eng. 23, 508–516 (2015). https://doi.org/10.1109/TNSRE.2014.2359997
Likforman-Sulem, L., Esposito, A., Faundez-Zanuy, M., Clemencon, S., Cordasco, G.: EMOTHAW: a novel database for emotional state recognition from handwriting and drawing. IEEE Trans. Hum.-Mach. Syst. 47, 273–284 (2017). https://doi.org/10.1109/THMS.2016.2635441
Improved Spiral Test Using Digitized Graphics Tablet for Monitoring Parkinson’s Disease. https://www.researchgate.net/publication/291814924_Improved_Spiral_Test_Using_Digitized_Graphics_Tablet_for_Monitoring_Parkinson’s_Disease. Accessed 13 Feb 2022
Impedovo, D., et al.: Writing Generation Model for Health Care neuromuscular System. 7° Convegno Nazionale di Viticoltura, Piacenza, 9–11 luglio 2018, p. 43 (2013)
Pereira, C.R., Weber, S.A.T., Hook, C., Rosa, G.H., Papa, J.P.: Deep learning-aided parkinson’s disease diagnosis from handwritten dynamics, pp. 340–346 (2016). https://doi.org/10.1109/SIBGRAPI.2016.054
Pereira, C.R., et al.: 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). https://doi.org/10.1109/CBMS.2015.34
Zhang, X.Y., Xie, G.S., Liu, C.L., Bengio, Y.: End-to-end online writer identification with recurrent neural network. IEEE Trans. Hum.-Mach. Syst. 47, 285–292 (2017). https://doi.org/10.1109/THMS.2016.2634921
Doetsch, P., Zeyer, A., Ney, H.: Bidirectional decoder networks for attention-based end-to-end offline handwriting recognition (2016)
Vaswani, A., et al.: Attention is all you need. CoRR. abs/1706.03762 https://arxiv.org/abs/1706.03762 (2017)
Puigcerver, J.: Are multidimensional recurrent layers really necessary for handwritten text recognition? In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, vol. 1, pp. 67–72 (2017). https://doi.org/10.1109/ICDAR.2017.20
Breiman, L.: Random forests (2001)
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Gattulli, V., Impedovo, D., Pirlo, G., Semeraro, G. (2022). Early Dementia Identification: On the Use of Random Handwriting Strokes. In: Carmona-Duarte, C., Diaz, M., Ferrer, M.A., Morales, A. (eds) Intertwining Graphonomics with Human Movements. IGS 2022. Lecture Notes in Computer Science, vol 13424. Springer, Cham. https://doi.org/10.1007/978-3-031-19745-1_21
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