A computer-based quantitative assessment of visuo-spatial neglect using regression and data transformation

Computer-based assessment systems analysing the drawing responses from a test subject have been widely explored within the area of neuropsychological dysfunction diagnosis and rehabilitation monitoring. This study reports on the development of a quantitative marking system for the automated assessme...

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Bibliographic Details
Published inPattern analysis and applications : PAA Vol. 13; no. 4; pp. 409 - 422
Main Authors Liang, Yiqing, Guest, Richard M., Fairhurst, Michael C., Potter, Jonathan M.
Format Journal Article
LanguageEnglish
Published London Springer-Verlag 01.11.2010
Springer
Subjects
Applied sciences
Artificial intelligence
Biological and medical sciences
Computer Science
Computer science; control theory; systems
Exact sciences and technology
Medical sciences
Pattern Recognition
Pattern recognition. Digital image processing. Computational geometry
Psychology. Psychoanalysis. Psychiatry
Psychometrics. Diagnostic aid systems
Psychopathology. Psychiatry
Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects)
Techniques and methods
Technology. Biomaterials. Equipments. Material. Instrumentation
Theoretical Advances
Medical diagnosis
Linear regression
Computer-based hand-drawing analysis
Data transformation
Visuo-spatial neglect
Regression analysis
Experimental study
Freehand sketch
Physical rehabilitation
Image analysis
Reeducation
Linear transformation
Diagnosis
Rehabilitation
Medical application
Pattern extraction
Monitoring
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Summary:Computer-based assessment systems analysing the drawing responses from a test subject have been widely explored within the area of neuropsychological dysfunction diagnosis and rehabilitation monitoring. This study reports on the development of a quantitative marking system for the automated assessment of visuo-spatial neglect. Using a clinically established pencil-and-paper based method as a marking benchmark, a set of features are extracted and selected from a battery of computer-captured drawing tasks. Through the application of linear regression and data transformation, the novel system is shown to be effective in correlating against a clinically recognised scale, while simultaneously improving the efficiency of the testing process.
ISSN:1433-7541
1433-755X
DOI:10.1007/s10044-009-0172-z