Efficacy of Guided Spiral Drawing in the Classification of Parkinson's Disease

Background: Change of handwriting can be an early marker for severity of Parkinson's disease but suffers from poor sensitivity and specificity due to inter-subject variations. Aim: This study has investigated the group-difference in the dynamic features during sketching of spiral between PD and control subjects with the aim of developing an accurate method for diagnosing PD patients. Method: Dynamic handwriting features were computed for 206 specimens collected from 62 Subjects (31 Parkinson's and 31 Controls). These were analyzed based on the severity of the disease to determine group-difference. Spearman rank correlation coefficient was computed to evaluate the strength of association for the different features. Results: Maximum area under ROC curve (AUC) using the dynamic features during different writing and spiral sketching tasks were in the range of 0.67 to 0.79. However, when angular features (<inline-formula><tex-math notation="LaTeX">$\boldsymbol{\varphi }$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">${\boldsymbol{p}_{\boldsymbol{n}}}$</tex-math></inline-formula>) and count of direction inversion during sketching of the spiral were used, AUC improved to 0.933. Spearman correlation coefficient was highest for <italic>ϕ</italic> and <inline-formula><tex-math notation="LaTeX"> ${\boldsymbol{p}_{\boldsymbol{n}}}$</tex-math></inline-formula>. Conclusion: The angular features and count of direction inversion which can be obtained in real-time while sketching the Archimedean guided spiral on a digital tablet can be used for differentiating between Parkinson's and healthy cohort.

[1]  Dietrich Haubenberger,et al.  Automatic Spiral Analysis for Objective Assessment of Motor Symptoms in Parkinson’s Disease , 2015, Sensors.

[2]  Peter Brown,et al.  Hypokinesia without decrement distinguishes progressive supranuclear palsy from Parkinson's disease. , 2012, Brain : a journal of neurology.

[3]  Martin Mozina,et al.  Orange: data mining toolbox in python , 2013, J. Mach. Learn. Res..

[4]  Seth L. Pullman,et al.  Spiral analysis—Improved clinical utility with center detection , 2008, Journal of Neuroscience Methods.

[5]  D. K. Kumar,et al.  Class specific dynamic feature selection technique — Towards human movement based biometrics application , 2013, 2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC).

[6]  Marcos Faúndez-Zanuy,et al.  Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease , 2016, Artif. Intell. Medicine.

[7]  Zdenek Smekal,et al.  Prediction potential of different handwriting tasks for diagnosis of Parkinson's , 2013, 2013 E-Health and Bioengineering Conference (EHB).

[8]  M Richards,et al.  Interrater reliability of the unified Parkinson's disease rating scale motor examination , 1994, Movement disorders : official journal of the Movement Disorder Society.

[9]  Abdulrahman H. Altalhi,et al.  Evaluation and comparison of open source software suites for data mining and knowledge discovery , 2017, WIREs Data Mining Knowl. Discov..

[10]  Clayton R. Pereira,et al.  A Step Towards the Automated Diagnosis of Parkinson's Disease: Analyzing Handwriting Movements , 2015, 2015 IEEE 28th International Symposium on Computer-Based Medical Systems.

[11]  Christine Klein,et al.  Digitized spiral analysis is a promising early motor marker for Parkinson Disease. , 2010, Parkinsonism & Related Disorders.

[12]  Annie McCluskey,et al.  A review of factors that influence adult handwriting performance. , 2011, Australian occupational therapy journal.

[13]  S. Swinnen,et al.  Opposite Effects of Visual Cueing During Writing-Like Movements of Different Amplitudes in Parkinson’s Disease , 2016, Neurorehabilitation and neural repair.

[14]  Dimitrios Hristu-Varsakelis,et al.  Machine learning-based classification of simple drawing movements in Parkinson's disease , 2017, Biomed. Signal Process. Control..

[15]  M. Samuel,et al.  Handwriting as an objective tool for Parkinson’s disease diagnosis , 2013, Journal of Neurology.

[16]  G E Stelmach,et al.  Discrete and dynamic scaling of the size of continuous graphic movements of parkinsonian patients and elderly controls , 2003, Journal of neurology, neurosurgery, and psychiatry.

[17]  Serge Pinto,et al.  From micrographia to Parkinson's disease dysgraphia , 2014, Movement disorders : official journal of the Movement Disorder Society.

[18]  G. Stelmach,et al.  Parkinsonian Patients Reduce Their Stroke Size with Increased Processing Demands , 2001, Brain and Cognition.

[19]  R. Lipton,et al.  Validity of spiral analysis in early Parkinson's disease , 2008, Movement disorders : official journal of the Movement Disorder Society.

[20]  P A Lewitt,et al.  Micrographia as a focal sign of neurological disease. , 1983, Journal of neurology, neurosurgery, and psychiatry.

[21]  Junzhong Zou,et al.  A new quantitative evaluation method of Parkinson's disease based on free spiral drawing , 2010, 2010 3rd International Conference on Biomedical Engineering and Informatics.

[22]  Won Yong Lee,et al.  Micrographia on free writing versus copying tasks in idiopathic Parkinson's disease. , 2005, Parkinsonism & related disorders.

[23]  Martijn Beudel,et al.  The Effect of Visual Feedback on Writing Size in Parkinson's Disease , 2015, Parkinson's disease.

[24]  Poonam Zham,et al.  Distinguishing Different Stages of Parkinson’s Disease Using Composite Index of Speed and Pen-Pressure of Sketching a Spiral , 2017, Front. Neurol..