Abstract
Alzheimer’s disease causes most of dementia cases. Although currently there is no cure for this disease, predicting the cognitive decline of people at the first stage of the disease allows clinicians to alleviate its burden. Clinicians evaluate individuals’ cognitive decline by using neuropsychological tests consisting of different sections, each devoted to test a specific set of cognitive skills. The sigma-lognormal model allows complex movements to be represented as a summation of simple time-overlapped movements, and has been used in several fields to model numerous human movements such as, for example, handwriting and speech. Recently, this theory has been also used for detecting and monitoring neurodegenerative disorders. In this paper, we present the results of a preliminary study aimed at exploring the use of lognormal features to classify patients affected by Alzheimer’s disease. The promising results achieved confirms that lognormal features can be used to support Alzheimer’s diagnosis.
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References
Albu, A., Precup, R., Teban, T.A.: Results and challenges of artificial neural networks used for decision making and control in medical applications. Facta Univ. Ser. Mech. Eng. 17(3), 285–308 (2019)
Carmona-Duarte, C., Ferrer, M., Plamondon, R., Gómez-Rodellar, A., Gómez-Vilda, P.: Sigma-lognormal modeling of speech. Cogn. Comput. 13(2), 488–503 (2021). https://doi.org/10.1007/s12559-020-09803-8
Cilia, N., D’Alessandro, T., De Stefano, C., Fontanella, F., Molinara, M.: From online handwriting to synthetic images for Alzheimer’s disease detection using a deep transfer learning approach. IEEE J. Biomed. Health Inform. 25(12), 4243–4254 (2021). https://doi.org/10.1109/JBHI.2021.3101982
Cilia, N.D., De Stefano, C., Fontanella, F., Di Freca, A.S.: An experimental protocol to support cognitive impairment diagnosis by using handwriting analysis. Proc. Comput. Sci. 141, 466–471 (2018)
Djioua, M., Plamondon, R.: A new algorithm and system for the characterization of handwriting strokes with delta-lognormal parameters. IEEE Trans. Pattern Anal. Mach. Intell. 31(11), 2060–2072 (2009)
Ferrer, M.A., Diaz, M., Carmona-Duarte, C., Plamondon, R.: IDeLog: iterative dual spatial and kinematic extraction of sigma-lognormal parameters. IEEE Trans. Pattern Anal. Mach. Intell. 42(1), 114–125 (2020)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Impedovo, D.: Velocity-based signal features for the assessment of parkinsonian handwriting. IEEE Signal Process. Lett. 26(4), 632–636 (2019)
Impedovo, D., Pirlo, G., Balducci, F., Dentamaro, V., Sarcinella, L., Vessio, G.: Investigating the sigma-lognormal model for disease classification by handwriting. In: The Lognormality Principle and its Applications in E-Security, E-Learning and E-Health, pp. 195–209. World Scientific (2021)
Impedovo, D., Pirlo, G., Vessio, G.: Dynamic handwriting analysis for supporting earlier Parkinson’s disease diagnosis. Information 9(10), 247 (2018)
Lebel, K., Nguyen, H., Duval, C., Plamondon, R., Boissy, P.: Capturing the cranio-caudal signature of a turn with inertial measurement systems: methods, parameters robustness and reliability. Front. Bioeng. Biotechnol. 5, 51 (2017). https://doi.org/10.3389/fbioe.2017.00051
Myszczynska, M.A., et al.: Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat. Rev. Neurol. 16, 440–456 (2020)
O’Reilly, C., Plamondon, R.: Design of a neuromuscular disorders diagnostic system using human movement analysis. In: 2012 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012, pp. 787–792 (2012). https://doi.org/10.1109/ISSPA.2012.6310660. Cited by: 28
O’Reilly, C., Plamondon, R.: Development of a sigma-lognormal representation for on-line signatures. Pattern Recogn. 42(12), 3324–3337 (2009). https://doi.org/10.1016/j.patcog.2008.10.017. new Frontiers in Handwriting Recognition
Plamondon, R.: A kinematic theory of rapid human movements - part II. Movement time and control. Biol. Cybern. 72(4), 309–320 (1995). https://doi.org/10.1007/BF00202786
Plamondon, R.: A kinematic theory of rapid human movements: part I. Movement representation and generation. Biol. Cybern. 72(4), 295–307 (1995)
Plamondon, R.: A kinematic theory of rapid human movements: part III. Kinetic outcomes. Biol. Cybern. 78(2), 133–145 (1998). https://doi.org/10.1007/s004220050420
Plamondon, R., O’Reilly, C., Rémi, C., Duval, T.: The lognormal handwriter: learning, performing, and declining. Front. Psychol. 4 (2013). https://doi.org/10.3389/fpsyg.2013.00945, https://www.frontiersin.org/article/10.3389/fpsyg.2013.00945
Plamondon, R., Pirlo, G., Anquetil, É., Rémi, C., Teulings, H.L., Nakagawa, M.: Personal digital bodyguards for e-security, e-learning and e-health: a prospective survey. Pattern Recogn. 81, 633–659 (2018). https://doi.org/10.1016/j.patcog.2018.04.012
Precup, R.E., Teban, T.A., Albu, A., Borlea, A.B., Zamfirache, I.A., Petriu, E.M.: Evolving fuzzy models for prosthetic hand myoelectric-based control. IEEE Trans. Instrum. Meas. 69(7), 4625–4636 (2020)
Schröter, A., Mergl, R., Bürger, K., Hampel, H., Möller, H.J., Hegerl, U.: Kinematic analysis of handwriting movements in patients with Alzheimer’s disease, mild cognitive impairment, depression and healthy subjects. Dement. Geriatr. Cogn. Disord. 15(3), 132–42 (2003)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR arXiv:1409.1556 (2015)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016)
Tanveer, M., et al.: Machine learning techniques for the diagnosis of Alzheimer’s disease: a review. ACM Trans. Multimed. Comput. Commun. Appl. 16(1s), 1–35 (2020)
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)
Vyhnálek, M., et al.: Clock drawing test in screening for Alzheimer’s dementia and mild cognitive impairment in clinical practice. Int. J. Geriatr. Psychiatry 32(9), 933–939 (2017)
Yan, J.H., Rountree, S., Massman, P., Smith Doody, R., Li, H.: Alzheimer’s disease and mild cognitive impairment deteriorate fine movement control. J. Psychiatr. Res. 42(14), 1203–1212 (2008)
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Cilia, N.D. et al. (2022). Lognormal Features for Early Diagnosis of Alzheimer’s Disease Through Handwriting Analysis. 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_24
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