Graphical representation and variability quantification of handwriting signals: New tools for Parkinson’s disease detection

• Autorzy: Dehghanpur Deharab Elham , Ghaderyan Peyvand

Identyfikatory

DOI

10.1016/j.bbe.2021.12.007

• Języki publikacji: EN

Abstrakty

EN

A reliable computer-aided method for Parkinson’s disease (PD) detection can slow down its progression and improve the life quality of patients. In this study, a new non-invasive and cost-effective method based on the online analysis of handwriting signals has been proposed. First, the dynamic handwriting signals have been converted into two graphical representations of the variability rate. Then, two new feasible features, including area of the analytic signal representation and area of the second-order difference plot, have been used to quantify the variability rate of handwriting signals. A statistical test and support vector machine classifier have been applied in a comparative study to test the impact of each variability feature, writing task, and time sequence on the detection performance, separately. The obtained results on PaHaW database with 35 Parkinson’s disease patients and 36 healthy controls have shown that the proposed method of handwriting variability feature extraction has effective performance and the capability for the PD detection. It has achieved an average sensitivity of 86.26% with only two types of features, providing a trade-off between the performance, the computational complexity, and interpretability of the motor patterns from the point of view of clinicians and neuropsychologists. Xcoordinate time-series and writing a sentence can achieve superior accuracy and robustness in the presence of individual differences. The experimental results have demonstrated that extracting the variability features that used graphical representations of the global changes in oscillatory mode has the ability to clinically describe the pathological dynamics of the handwriting signals for the PD identification.

Słowa kluczowe

EN

second order difference plot; analytic signal representation; area measures; empirical mode decomposition

PL

wykres różnicowy drugiego rzędu; pomiar powierzchni; dekompozycja empiryczna

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 1
• Strony: 158--172
• Opis fizyczny: Bibliogr. 53 poz., rys., tab.

Twórcy

autor

Dehghanpur Deharab Elham

• Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran

autor

Ghaderyan Peyvand

• Computational Neuroscience Laboratory, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran

Bibliografia

• [1] Golbe LI, Mark MH, Sage J. Parkinson’s Disease Handbook. American Parkinson Disease Association; 2009.
• [2] Tysnes O-B, Storstein A. Epidemiology of Parkinson’s disease. J Neural Transm 2017;124:901–5.
• [3] 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 2019;121:37–45.
• [4] Niethammer M, Feigin A, Eidelberg D. Functional neuroimaging in Parkinson’s disease. Cold Spring Harbor perspectives in medicine. 2012;2:a009274.
• [5] Hughes AJ, Daniel SE, Ben-Shlomo Y, Lees AJ. The accuracy of diagnosis of parkinsonian syndromes in a specialist movement disorder service. Brain 2002;125:861–70.
• [6] De Stefano C, Fontanella F, Impedovo D, Pirlo G, di Freca AS. A Brief Overview on Handwriting Analysis for Neurodegenerative Disease Diagnosis. WAIAH@ AI* IA2017. p. 9-16.
• [7] Sahni S, Aggarwal V, Khanna A, Gupta D, Bhattacharyya S. Quantum-inspired evolutionary algorithms for neural network weight distribution: a classification model for Parkinson’s disease. J Inf Org Sci 2020;44:345–63.
• [8] Gupta D, Julka A, Jain S, Aggarwal T, Khanna A, Arunkumar N, et al. Optimized cuttlefish algorithm for diagnosis of Parkinson’s disease. Cognit Syst Res 2018;52:36–48.
• [9] Gupta D, Sundaram S, Khanna A, Ella Hassanien A, de Albuquerque VHC. Improved diagnosis of Parkinson’s disease using optimized crow search algorithm. Comput Electr Eng 2018;68:412–24.
• [10] Ghaderyan P, 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;114:428–40.
• [11] Faundez-Zanuy M, Fierrez J, Ferrer MA, Diaz M, Tolosana R, Plamondon R. Handwriting biometrics: applications and future trends in e-security and e-health. Cognitive Computation 2020;12:940–53.
• [12] Letanneux A, Danna J, Velay J-L, Viallet F, Pinto S. From micrographia to Parkinson’s disease dysgraphia. Mov Disord 2014;29:1467–75.
• [13] 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 (MeMeA) Proceedings. IEEE; 2015. p. 344–8.
• [14] Saunders-Pullman R, Derby C, Stanley K, Floyd A, Bressman S, Lipton RB, et al. Validity of spiral analysis in early Parkinson’s disease. Movement Disorders 2008;23:531–7.
• [15] Broderick MP, Van Gemmert AWA, Shill HA, Stelmach GE. Hypometria and bradykinesia during drawing movements in individuals with Parkinson’s disease. Exp Brain Res 2009;197:223–33.
• [16] 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 2016;67:39–46.
• [17] Lange KW, Mecklinger L, Walitza S, Becker G, Gerlach M, Naumann M, et al. Brain dopamine and kinematics of graphomotor functions. Hum Mov Sci 2006;25:492–509.
• [18] Kim E-J, Lee BH, Park KC, Lee WY, Na DL. Micrographia on free writing versus copying tasks in idiopathic Parkinson’s disease. Parkinsonism Related Disord 2005;11:57–63.
• [19] Eskov VV, Filatova OE, Gavrilenko TV, Gorbunov DV. Chaotic dynamics of neuromuscular system parameters and the problems of the evolution of complexity. Biophysics 2017;62:961–6.
• [20] Drotar P, Mekyska J, Rektorova I, Masarova L, Smekal Z, Faundez-Zanuy M. Decision support framework for Parkinson’s disease based on novel handwriting markers. IEEE Trans Neural Syst Rehabil Eng 2015;23:508–16.
• [21] Stergiou N, Decker LM. Human movement variability, nonlinear dynamics, and pathology: is there a connection? Hum Mov Sci 2011;30:869–88.
• [22] Amoud H, Snoussi H, Hewson DJ, Duchene J. Hilbert-Huang transformation: application to postural stability analysis. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE; 2007. p. 1562–5.
• [23] Mandal S, Prasanna SRM, Sundaram S. GMM posterior features for improving online handwriting recognition. Expert Syst Appl 2018;97:421–33.
• [24] Pachori RB, Bajaj V. Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition. Comput Methods Programs Biomed 2011;104:373–81.
• [25] Thuraisingham RA, Tran Y, Boord P, Craig A. Analysis of eyes open, eye closed EEG signals using second-order difference plot. Med Biol Eng Compu 2007;45:1243–9.
• [26] Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc Royal Soc London Series A: Math Phys Eng Sci 1998;454:903–95.
• [27] Pachori RB, Avinash P, Shashank K, Sharma R, Acharya UR. Application of empirical mode decomposition for analysis of normal and diabetic RR-interval signals. Expert Syst Appl 2015;42:4567–81.
• [28] Impedovo D, Pirlo G. Dynamic handwriting analysis for the assessment of neurodegenerative diseases: a pattern recognition perspective. IEEE Rev Biomed Eng 2019;12:209–20.
• [29] Drotár P, Mekyska J, Rektorová I, Masarová L, Smékal Z, Faundez-Zanuy M. Analysis of in-air movement in handwriting: a novel marker for Parkinson’s disease. Comput Methods Programs Biomed 2014;117:405–11.
• [30] Krishnan S, Athavale Y. Trends in biomedical signal feature extraction. Biomed Signal Process Control 2018;43:41–63.
• [31] Ghaderyan P, Abbasi A. Dynamic Hilbert warping, a new measure of RR-interval signals evaluated in the cognitive load estimation. Med Eng Phys 2017;40:103–9.
• [32] Amoud H, Snoussi H, Hewson D, Duchêne J. Univariate and bivariate empirical mode decomposition for postural stability analysis. EURASIP J Adv Signal Process 2008;2008:1–11.
• [33] Cavalheiro GL, Almeida MFS, Pereira AA, Andrade AO. Study of age-related changes in postural control during quiet standing through linear discriminant analysis. Biomed Eng Online 2009;8:35.
• [34] Khatamino P, Cantürk İ, Özyılmaz L. A deep learning-CNN based system for medical diagnosis: an application on Parkinson’s Disease handwriting drawings. In: 2018 6th International Conference on Control Engineering & Information Technology (CEIT). IEEE; 2018. p. 1–6.
• [35] Pereira CR, Pereira DR, Rosa GH, Albuquerque VHC, Weber SAT, Hook C, et al. Handwritten dynamics assessment through convolutional neural networks: an application to Parkinson’s disease identification. Artif Intell Med 2018;87:67–77.
• [36] Impedovo D. Velocity-based signal features for the assessment of Parkinsonian handwriting. IEEE Signal Process Lett 2019;26:632–6.
• [37] Vapnik V. The nature of statistical learning theory. Springer science & business media; 2013.
• [38] Cherkassky V, Mulier FM. Learning from data: concepts, theory, and methods. John Wiley & Sons; 2007.
• [39] Tang Y, Zhang Y-Q, Chawla NV, Krasser S. SVMs modeling for highly imbalanced classification. IEEE Trans Systems Man Cybernetics Part B (Cybernetics) 2009;39:281–8.
• [40] Ghaderyan P, Abbasi A, Sedaaghi MH. An efficient seizure prediction method using KNN-based undersampling and linear frequency measures. J Neurosci Methods 2014;232:134–42.
• [41] Chang C-C, Lin C-J. LIBSVM: A library for support vector machines. ACM Trans Intel Syst Technol (TIST) 2011;2:1–27.
• [42] Drotár P, Mekyska J, Smékal Z, Rektorová I, Masarová L, Faundez-Zanuy M. Prediction potential of different handwriting tasks for diagnosis of Parkinson’s. In: 2013 EHealth and Bioengineering Conference (EHB). IEEE; 2013. p. 1–4.
• [43] 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 Series B: Psychol Sci Social Sci 2006;61:P228–36.
• [44] Drotár P, Mekyska J, Rektorová I, Masarová L, Smékal Z, Faundez-Zanuy M. A new modality for quantitative evaluation of Parkinson’s disease: In-air movement. In: 13th IEEE international conference on bioinformatics and bioengineering. IEEE; 2013. p. 1–4.
• [45] Diaz M, Moetesum M, Siddiqi I, Vessio G. Sequence-based dynamic handwriting analysis for Parkinson’s disease detection with one-dimensional convolutions and BiGRUs. Expert Syst Appl 2021;168:114405.
• [46] Senatore R, Marcelli A. A paradigm for emulating the early learning stage of handwriting: performance comparison between healthy controls and Parkinson’s disease patients in drawing loop shapes. Hum Mov Sci 2019;65:89–101.
• [47] Diaz M, Ferrer MA, Impedovo D, Pirlo G, Vessio G. Dynamically enhanced static handwriting representation for Parkinson’s disease detection. Pattern Recogn Lett 2019;128:204–10.
• [48] Goyal J, Khandnor P, Aseri TC. Classification, prediction, and monitoring of Parkinson’s disease using computer assisted technologies: a comparative analysis. Eng Appl Artif Intell 2020;96:103955.
• [49] Pereira CR, Weber SA, Hook C, Rosa GH, Papa JP. Deep learning-aided Parkinson’s disease diagnosis from handwritten dynamics. In: 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). Ieee; 2016. p. 340–6.
• [50] Pereira CR, Pereira DR, Papa JP, Rosa GH, Yang X-S. Convolutional neural networks applied for Parkinson’s disease identification. In: Machine learning for health informatics. Springer; 2016. p. 377–90.
• [51] Moetesum M, Siddiqi I, Vincent N, Cloppet F. Assessing visual attributes of handwriting for prediction of neurological disorders—A case study on Parkinson’s disease. Pattern Recogn Lett 2019;121:19–27.
• [52] Naseer A, Rani M, Naz S, Razzak MI, Imran M, Xu G. Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Comput Appl 2020;32:839–54.
• [53] Yoon JH, Suh MK, Jeong Y, Ahn H-J, Moon SY, Chin J, et al. Agraphia in Korean patients with early onset Alzheimer’s disease. Int Psychogeriatr 2011;23:1317–26.