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Volume 22
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10.3390/s22041688
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Article

Computer Aided Written Character Feature Extraction in Progressive Supranuclear Palsy and Parkinson’s Disease

by
Paula Stępień
1,
Jacek Kawa
1,*,
Emilia J. Sitek
2,3,
Dariusz Wieczorek
4,
Rafał Sikorski
5,
Magda Dąbrowska
3,
Jarosław Sławek
2,3 and
Ewa Pietka
1
1
Faculty of Biomedical Engineering, Silesian University of Technology, 41-800 Zabrze, Poland
2
Division of Neurological and Psychiatric Nursing, Faculty of Health Sciences, Medical University of Gdansk, 80-211 Gdansk, Poland
3
Department of Neurology, St. Adalbert Hospital, Copernicus PL Ltd., 80-462 Gdansk, Poland
4
Department of Rehabilitation, Faculty of Health Sciences, Medical University of Gdansk, 80-219 Gdansk, Poland
5
Department of Rehabilitation, Saint Vincent a Paulo Hospital, Pomeranian Hospitals Ltd., 81-519 Gdynia, Poland
*
Author to whom correspondence should be addressed.
Sensors 2022, 22(4), 1688; https://doi.org/10.3390/s22041688
Submission received: 5 January 2022 / Revised: 16 February 2022 / Accepted: 17 February 2022 / Published: 21 February 2022
(This article belongs to the Special Issue Innovations in Biomedical Imaging)
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Abstract

Parkinson’s disease (PD) and progressive supranuclear palsy (PSP) are neurodegenerative movement disorders associated with cognitive dysfunction. The Luria’s Alternating Series Test (LAST) is a clinical tool sensitive to both graphomotor problems and perseverative tendencies that may suggest the dysfunction of prefrontal and/or frontostriatal areas and may be used in PD and PSP assessment. It requires the participant to draw a series of alternating triangles and rectangles. In the study, two clinical groups—51 patients with PD and 22 patients with PSP—were compared to 32 neurologically intact seniors. Participants underwent neuropsychological assessment. The LAST was administered in a paper and pencil version, then scanned and preprocessed. The series was automatically divided into characters, and the shapes were recognized as rectangles or triangles. In the feature extraction step, each rectangle and triangle was regarded both as an image and a two-dimensional signal, separately and as a part of the series. Standard and novel features were extracted and normalized using characters written by the examiner. Out of 71 proposed features, 51 differentiated the groups (p < 0.05). A classifier showed an accuracy of 70.5% for distinguishing three groups.
Keywords: baseline estimation; character recognition; computer aided diagnosis; neurodegenerative diseases; pattern analysis; writing analysis baseline estimation; character recognition; computer aided diagnosis; neurodegenerative diseases; pattern analysis; writing analysis

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MDPI and ACS Style

Stępień, P.; Kawa, J.; Sitek, E.J.; Wieczorek, D.; Sikorski, R.; Dąbrowska, M.; Sławek, J.; Pietka, E. Computer Aided Written Character Feature Extraction in Progressive Supranuclear Palsy and Parkinson’s Disease. Sensors 2022, 22, 1688. https://doi.org/10.3390/s22041688

AMA Style

Stępień P, Kawa J, Sitek EJ, Wieczorek D, Sikorski R, Dąbrowska M, Sławek J, Pietka E. Computer Aided Written Character Feature Extraction in Progressive Supranuclear Palsy and Parkinson’s Disease. Sensors. 2022; 22(4):1688. https://doi.org/10.3390/s22041688

Chicago/Turabian Style

Stępień, Paula, Jacek Kawa, Emilia J. Sitek, Dariusz Wieczorek, Rafał Sikorski, Magda Dąbrowska, Jarosław Sławek, and Ewa Pietka. 2022. "Computer Aided Written Character Feature Extraction in Progressive Supranuclear Palsy and Parkinson’s Disease" Sensors 22, no. 4: 1688. https://doi.org/10.3390/s22041688

APA Style

Stępień, P., Kawa, J., Sitek, E. J., Wieczorek, D., Sikorski, R., Dąbrowska, M., Sławek, J., & Pietka, E. (2022). Computer Aided Written Character Feature Extraction in Progressive Supranuclear Palsy and Parkinson’s Disease. Sensors, 22(4), 1688. https://doi.org/10.3390/s22041688

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