Task-related handwriting and drawing features for early detection of Alzheimer’s disease: A pilot study

Abstract

Background: Dementia causes significant disability worldwide and has no cure. The only way to improve the quality of life of those affected is through early intervention. For this reason, the development of effective diagnostic tools is a priority for healthcare systems and researchers. Handwriting and drawing, which engage multiple cognitive and motor areas, have shown promise in detecting early signs of dementia. However, findings in this field remain inconsistent, largely due to a lack of standardized protocols. Aim: This study aims to investigate the discriminatory power of graphomotor analysis in distinguishing individuals with Alzheimer’s disease (AD) from healthy controls (HC) by examining the contribution of dynamic handwriting features and task-related characteristics within an easy-to-use and multi-task protocol. Methods: Patients with AD (n = 14) and HC (n = 25) were asked to complete five drawing and two writing tasks, and their online data were recorded using a digital tablet. Results: Significant differences (p<0.05) between groups were observed for time- and ductus-related features in almost all tasks, while pressure, space, and inclination features did not differ significantly. Conclusion: Although certain graphomotor characteristics are more sensitive than others, analyzing them together yields a detailed functional profile of patients. Overall, the study provides evidence of the effectiveness of handwriting analysis in identifying several symptoms associated with dementia. The protocol warrants further validation with a larger sample. Relevance for patients: The proposed protocol highlights the potential of a handwriting-based tool as an ecologically valid, objective, and accessible method for assessing and monitoring dementia. Adopting up-to-date digital approaches responds to the need for more sensitive tools that align with technological and cultural changes within the population. This could consequently simplify screening, improve access to treatment, and enhance the quality of life for patients and their caregivers.

Keywords

Dementia screening / Alzheimer’s disease / Handwriting analysis / Online feature / Kinematic parameters

Funding

This study received funding from the EU-H2020 program (grant no. 101182965, CRYSTAL), the EU NextGenerationE PNRR Mission 4 Component 2 Investment 1.1 (D.D 1409 del 14-09-2022) PRIN 2022 under the IRRESPECTIVE project (code P20222MYKE, CUP: B53D23025980001), and PNRR MUR under AI-PATTERNS FAIR Project (E63C22002150007).

Conflict of interest

The authors declare that they have no competing interests.

References

[1] Smith AD. On the use of drawing tasks in neuropsychological assessment. Neuropsychology. 2009; 23(2):231-239. doi: 10.1037/a0014184

[2] Yu NY, Chang SH. Kinematic analyses of graphomotor functions in individuals with Alzheimer’s disease and amnestic mild cognitive impairment. J Med Biol Eng. 2016; 36:334-343. doi: 10.1007/s40846-016-0143-y

[3] Ghaderyan S, Abbasi A, Saber S. A new algorithm for kinematic analysis of handwriting data; towards a reliable handwriting-based tool for early detection of Alzheimer’s disease. Expert Syst Appl. 2018; 114:428-440. doi: 10.1016/j.eswa.2018.07.052

[4] Kairamkonda DD, Mandeleeka PS, Favaro A, et al. Analysis of interpretable handwriting features to evaluate motoric patterns in different neurodegenerative diseases. In: 2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). Philadelphia, PA, USA: IEEE; 2022. p. 1-6. doi: 10.1109/spmb55497.2022.10014966

[5] Slavin MJ, Phillips JG, Bradshaw JL, Hall KA, 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. 1999; 5:20-25. doi: 10.1017/s135561779951103x

[6] Kahindo C, El-Yacoubi MA, Garcia-Salicetti S, Rigaud AS, Cristancho-Lacroix V. Characterizing early-stage Alzheimer through spatiotemporal dynamics of handwriting. IEEE Signal Process Lett. 2018; 25(8):1136-1140. doi: 10.1109/lsp.2018.2794500

[7] Impedovo D, Pirlo G, Barbuzzi D, Balestrucci A, Impedovo S. Handwritten processing for pre diagnosis of Alzheimer disease. In: Proceedings of Biodevices 2014; 2014. p. 193-199. doi: 10.5220/0004900701930199

[8] Plonka A, Mouton A, Macoir J, et al. Primary progressive aphasia: Use of graphical markers for an early and differential diagnosis. Brain Sci. 2021; 11(9):1198. doi: 10.3390/brainsci11091198

[9] Bensalah A, Parziale A, De Gregorio G, Marcelli A, Fornés A, Lladós J. I can’t believe it’s not better: In-air movement for Alzheimer handwriting synthetic generation. In: Proceedings of the International Graphonomics Conference. Cham, Switzerland; 2023. p. 136-148. doi: 10.48550/arXiv.2312.05086

[10] Cilia ND, De Stefano C, Fontanella F, Di Freca AS. An experimental protocol to support cognitive impairment diagnosis by using handwriting analysis. Procedia Comput Sci. 2018; 141:466-471. doi: 10.1016/j.procs.2018.10.141

[11] Cilia ND, De Gregorio G, De Stefano C, Fontanella F, Marcelli A, Parziale A. Diagnosing Alzheimer’s disease from on-line handwriting: A novel dataset and performance benchmarking. Eng Appl Artif Intell. 2022; 111:104822. doi: 10.1016/j.engappai.2022.104822

[12] Likforman-Sulem L, Esposito A, Faundez-Zanuy M, Clémençon S, Cordasco G. EMOTHAW: A novel database for emotional state recognition from handwriting and drawing. IEEE Trans Hum-Mach Syst. 2017; 47(2):273-284. doi: 10.1109/thms.2016.2635441

[13] Impedovo D, Pirlo G. Dynamic handwriting analysis for the assessment of neurodegenerative diseases: A pattern recognition perspective. IEEE Rev Biomed Eng. 2019; 12:209-220. doi: 10.1109/rbme.2018.2840679

[14] Schröter A, Mergl R, Bürger K, Hampel H, Möller HJ, Hegerl U. Kinematic analysis of handwriting movements in patients with Alzheimer’s disease, mild cognitive impairment, depression and healthy subjects. Dement Geriatr Cogn Disord. 2003; 15(3):132-142. doi: 10.1159/000068484

[15] Qi H, Zhang R, Wei Z, et al. A study of auxiliary screening forAlzheimer’s disease based on handwriting characteristics. Front Aging Neurosci. 2023; 15:1117250. doi: 10.3389/fnagi.2023.1117250

[16] Kawa J, Bednorz A, Stępień P, Derejczyk J, Bugdol M. Spatial and dynamical handwriting analysis in mild cognitive impairment. Comput Biol Med. 2017; 82:21-28. doi: 10.1016/j.compbiomed.2017.01.004

[17] Garre-Olmo J, Faúndez-Zanuy M, López-De-Ipiña K, Calvó-Perxas L, Turró-Garriga O. Kinematic and pressure features of handwriting and drawing: Preliminary results between patients with mild cognitive impairment, Alzheimer disease and healthy controls. Curr Alzheimer Res. 2017; 14(9):960-968. doi: 10.2174/1567205014666170309120708

[18] Müller S, Preische O, Heymann P, Elbing U, Laske C. Diagnostic value of a tablet-based drawing task for discrimination of patients in the early course of Alzheimer’s disease from healthy individuals. J Alzheimers Dis. 2016; 55(4):1463-1469. doi: 10.3233/jad-160921

[19] Yamada Y, Shinkawa K, Kobayashi M, et al. Combining multimodal behavioral data of gait, speech, and drawing for classification of Alzheimer’s disease and mild cognitive impairment. J Alzheimers Dis. 2021; 84(1):315-327. doi: 10.3233/jad-210684

[20] McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. 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. 1984; 34(7):939-944. doi: 10.1212/wnl.34.7.939

[21] American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 5^th ed. Arlington, VA: American Psychiatric Association; 2013.

[22] Petersen RC, Jack CR Jr., Xu YC, et al. Memory and MRI-based hippocampal volumes in aging and AD. Neurology. 2000; 54(3):581-587. doi: 10.1212/wnl.54.3.581

[23] Folstein MF, Robins LN, Helzer JE. The mini-mental state examination. Arch Gen Psychiatry. 1983; 40(7):812. doi: 10.1001/archpsyc.1983.01790060110016

[24] Nasreddine ZS, Phillips NA, Bédirian V, et al. The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005; 53(4):695-699. doi: 10.1111/j.1532-5415.2005.53221.x

[25] Conti S, Bonazzi S, Laiacona M, Masina M, Coralli MV. Montreal Cognitive Assessment (MoCA)-Italian version: Regression based norms and equivalent scores. Neurol Sci. 2015; 36(2):209-214. doi: 10.1007/s10072-014-1921-3

[26] Carson N, Leach L, Murphy KJ. A re-examination of montreal cognitive assessment (MoCA) cutoff scores. Int J Geriatr Psychiatry. 2018; 33(2):379-388. doi: 10.1002/gps.4756

[27] Measso G, Cavarzeran F, Zappala G, et al. The mini-mental state examination: Normative study of an Italian random sample. Dev Neuropsychol. 1993; 9(2):77-85. doi: 10.1080/87565649109540545

[28] De Paula JJ, Albuquerque MR, Lage GM, Bicalho MA, Romano-Silva MA, Malloy-Diniz LF. Impairment of fine motor dexterity in mild cognitive impairment and Alzheimer’s disease dementia: Association with activities of daily living. Braz J Psychiatry. 2016; 38:235-238. doi: 10.1590/1516-4446-2015-1874

[29] Neils-Strunjas J, Groves-Wright K, Mashima P, Harnish S. Dysgraphia in Alzheimer’s disease: A review for clinical and research purposes. J Speech Lang Hear Res. 2006; 49(6):1313-1330. doi: 10.1044/1092-4388(2006/094)

[30] Yan JH, Rountree S, Massman P, Doody RS, Li H. Alzheimer’s disease and mild cognitive impairment deteriorate fine movement control. J Psychiatr Res. 2008; 42(14):1203-1212. doi: 10.1016/j.jpsychires.2008.01.006

[31] Guarino A, Favieri F, Boncompagni I, Agostini F, Cantone M, Casagrande M. Executive functions in Alzheimer disease: A systematic review. Front Aging Neurosci. 2019; 10:437. doi: 10.3389/fnagi.2018.00437

[32] Fernandes CP, Montalvo G, Caligiuri M, Pertsinakis M, Guimarães J. Handwriting changes in Alzheimer’s disease: A systematic review. J Alzheimers Dis. 2023; 96(1):1-11. doi: 10.3233/JAD-230438

[33] 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 B Psychol Sci Soc Sci. 2006; 61(4):P228-P236. doi: 10.1093/geronb/61.4.p228

[34] Greco C, Raimo G, Amorese T, et al. Discriminative power of handwriting and drawing features in depression. Int J Neural Syst. 2024; 34(2):2350069. doi: 10.1142/s0129065723500697

[35] Delazer M, Zamarian L, Djamshidian A. Handwriting in Alzheimer’s disease. J Alzheimers Dis. 2021; 82(2):727-735. doi: 10.3233/jad-210279

[36] Koppelmans V, Silvester B, Duff K. Neural mechanisms of motor dysfunction in mild cognitive impairment and Alzheimer’s disease: A systematic review. J Alzheimers Dis Rep. 2022; 6(1):307-344. doi: 10.3233/adr-210065

[37] Crockett RA, Hsu CL, Best JR, Beauchet O, Liu-Ambrose T. Head over heels but I forget why: Disruptive functional connectivity in older adult fallers with mild cognitive impairment. Behav Brain Res. 2019; 376:112104. doi: 10.1016/j.bbr.2019.112104

[38] Kobayashi M, Yamada Y, Shinkawa K, Nemoto M, Nemoto K, Arai T. Automated early detection of Alzheimer’s disease by capturing impairments in multiple cognitive domains with multiple drawing tasks. J Alzheimers Dis. 2022; 88(3):1075-1089. doi: 10.3233/jad-215714