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

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Abstract

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.

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References

  1. Cherney LR, Halper AS (2001) Unilateral visual neglect in right-hemisphere stroke: a longitudinal study. Brain Inj 15:585–592

    Article  Google Scholar 

  2. Cassidy TP, Lewis S, Gray CS (1998) Recovery from visuospatial neglect in stroke patients. J Neural Neurosurg Psychiatry 64:555–557

    Article  Google Scholar 

  3. Halligan PW, Robertson IH (1992) The assessment of unilateral neglect. In: Crawford JR, Parker DM, McKinlay WW (eds) A handbook of neuropsychological assessment. Psychology Press, UK

    Google Scholar 

  4. Wilson B, Cockburn J, Halligan PW, Psych D (1987) Development of a behavioral test of visuospatial neglect. Arch Phys Med Rehabil 68:98–102

    Google Scholar 

  5. Fairhurst MC, Smith SL (1991) Application of image analysis to neurological screening through figure-copying tasks. Int J Biomed Comput 28:269–287

    Article  Google Scholar 

  6. Chindaro S, Guest RM, Fairhurst MC, Potter J (2004) Assessing visuo-spatial neglect through feature selection and combination from geometric shape drawing performance and sequence analysis. Int J Pattern Recognit Artif Intell 18:1253–1266

    Article  Google Scholar 

  7. Halligan PW, Marshall JC, Wade DT (1989) Visuo-spatial neglect-underlying factors and test sensitivity. Lancet 2:908–911

    Article  Google Scholar 

  8. Teasdale TW, Hansen HS, Gade A, Christensen AL (1997) Neuropsychological test scores before and after brain-injury rehabilitation in relation to return to employment. Neuropsychol Rehabil 7:23–42

    Article  Google Scholar 

  9. Hannaford S, Gower G, Potter JM, Guest RM, Fairhurst MC (2003) Assessing visual inattention: study of inter-rater reliability. Br J Ther Rehabil 10(2):72–76

    Google Scholar 

  10. Guest RM, Fairhurst MC, Potter JM, Donnelly N (2000) Analysing constructional aspects of figure completion for the diagnosis of visuospatial neglect. ICPR 4:4316–4319

    Google Scholar 

  11. Guest RM, Fairhurst MC (2002) A novel multi-stage approach to the detection of visuo-spatial neglect based on the analysis of figure-copying tasks. ASSETS, Edinburgh, pp 157–161

  12. Guest RM, Chindaro S, Fairhurst MC, Potter JM (2003) Automatic classification of hand drawn geometric shapes using constructional sequence analysis. In: Proceedings of ICDAR

  13. Liang Y, Guest RM, Fairhurst MC, Potter JM (2007) Feature-based assessment of visuo-spatial neglect patients using hand-drawing tasks. Pattern Anal Appl 10(4):361–374

    Article  MathSciNet  Google Scholar 

  14. Fairhurst MC, Guest RM, Donnelly N, Potter J, Deighton A, Patel M (1998) Engineering software tools for assessment of visuo-spatial neglect. In: Proceedings of IEEE colloquium on intelligent decision support in clinical practice. London

  15. Martens H, Naes T (1989) Multivariate Calibration. Wiley, New York

    MATH  Google Scholar 

  16. Potter JM, Deighton A, Patel M, Fairhurst MC, Guest RM, Donnelly N (2000) Computer recording of standard test of visual neglect in stroke patients. Clin Rehabil 14:441–446

    Article  Google Scholar 

  17. Mosteller F, Tukey JW (1977) Data analysis and regression. Addison-Wesley Pub Co, Massachusetts

    Google Scholar 

  18. Witten IH (2000) Data mining: Practical machine learning tools and techniques with JAVA implementations. Morgan Kaufmann, San Francisco

    Google Scholar 

  19. Weisberg S (1985) Applied linear regression. Wiley, New York

    MATH  Google Scholar 

  20. Jolliffe IT (1982) A note on the use of principal components in regression. Appl Stat 31(3):300–303

    Article  Google Scholar 

  21. Hawkins DM (1973) On the investigation of alternative regressions by principal component analysis. Appl Stat 22(3):275–286

    Article  MathSciNet  Google Scholar 

  22. Nello C, John S-T (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge

  23. Arya S, Mount DM, Netanyahu NS, Silverman R, Wu. AY (1998) An optimal algorithm for approximate nearest neighbor searching fixed dimensions. J ACM 45(6):891–923

    Article  MATH  MathSciNet  Google Scholar 

  24. Elkan C (1997) Boosting and naive bayesian learning (Technical Report). Department of Computer Science and Engineering, University of California, San Diego

  25. Altman DG (1991) Practical Statistics for Medical Research. Chapman & Hall, London

    Google Scholar 

  26. Feinstein AR (2002) Principles of medical statistics. CRC Press, London

  27. Faundez-Zaiiuy M (2005) Data fusion in biometrics. IEEE Aerosp Electron Syst Mag 20(1):34–38

    Article  Google Scholar 

  28. Hocking RR (1976) The analysis and selection of variables in linear regression. Biometrics 32:1–49

    Article  MATH  MathSciNet  Google Scholar 

  29. Partridge M (1998) Fast dimensionality reduction and simple PCA. Intelligent data analysis. 2(3):1–12

    Google Scholar 

  30. Norusis MJ (2004) SPSS 12.0 guide to data analysis. Prentice Hall, New Jersey

    Google Scholar 

  31. Martens H, Dardenne P (1998) Validation and verification of regression in small data sets. Chemom Intell Lab Syst 44:99–121

    Article  Google Scholar 

  32. Bradley JV (1968) Distribution-free statistical tests. Prentice-Hall, New Jersey

    MATH  Google Scholar 

  33. Massey FJ (1951) The Kolmogorov–Smirnov test for goodness of fit. J Am Stat Assoc 46:68–78

    Article  MATH  Google Scholar 

  34. Han J, Kamber M (2001) Data mining concepts and techniques. Morgan Kaufmann Publishers, San Francisco

    Google Scholar 

  35. De Maesschalck R, Estienne F, Verdú-Andrés J et al (1999) The development of calibration models for spectroscopic data using principal component regression. Internet J Chem 2:19

    Google Scholar 

  36. Hadi AS, Ling RF (1998) Some cautionary notes on the use of principal components regression. Am Stat 52(1):15–19

    Article  Google Scholar 

  37. Bartolomeo P, D’Erme P, Perri R, Gainotti G (1998) Perception and action in hemispatial neglect. Neuropsychologia 36(3):227–237

    Article  Google Scholar 

  38. Jackson SR, Newport R, Husain M, Harvey M, Hindle JV (2000) Reaching movements may reveal the distorted topography of spatial representations after neglect. Neuropsychologia 38(4):500–507

    Article  Google Scholar 

  39. Chan YH (2003) Biostatistics 104: correlational analysis. Singapore Med J 44:614–619

    Google Scholar 

  40. Cohen J (1960) A coefficient of agreement for nominal scale. Educ Psychol Meas 20:37–46

    Article  Google Scholar 

  41. Bengio S, Mariéthoz J (2004) The expected performance curve: a new assessment measure for person authentication. In: Proceedings of Odyssey 2004: the speaker and language recognition workshop, pp 9–16

  42. Albert ML (1973) A simple test of visual neglect. Neurology 23:658–664

    Google Scholar 

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Acknowledgments

The authors gratefully acknowledge the support of the East Kent Hospitals NHS Trust Charitable Fund.

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Authors and Affiliations

  1. Department of Electronics, University of Kent, Canterbury, UK

    Yiqing Liang, Richard M. Guest & Michael C. Fairhurst

  2. Kent and Canterbury Hospital, Canterbury, UK

    Jonathan M. Potter

Authors
  1. Yiqing Liang
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  2. Richard M. Guest
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  3. Michael C. Fairhurst
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  4. Jonathan M. Potter
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Corresponding author

Correspondence to Richard M. Guest.

Appendix

Appendix

List of computer-based features are given in Tables 6 and 7.

Table 6 Features extracted from Cancellation tasks
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Table 7 Features extracted from figure copying, figure completion and drawing from memory tasks
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Liang, Y., Guest, R.M., Fairhurst, M.C. et al. A computer-based quantitative assessment of visuo-spatial neglect using regression and data transformation. Pattern Anal Applic 13, 409–422 (2010). https://doi.org/10.1007/s10044-009-0172-z

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  • DOI: https://doi.org/10.1007/s10044-009-0172-z

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