Skip to main content

Advertisement

SpringerLink
Log in

Rank Determination of Mental Functions by 1D Wavelets and Partial Correlation

  • Patient Facing Systems
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

The main aim of this paper is to classify mental functions by the Wechsler Adult Intelligence Scale-Revised tests with a mixed method based on wavelets and partial correlation. The Wechsler Adult Intelligence Scale-Revised is a widely used test designed and applied for the classification of the adults cognitive skills in a comprehensive manner. In this paper, many different intellectual profiles have been taken into consideration to measure the relationship between the mental functioning and psychological disorder. We propose a method based on wavelets and correlation analysis for classifying mental functioning, by the analysis of some selected parameters measured by the Wechsler Adult Intelligence Scale-Revised tests. In particular, 1-D Continuous Wavelet Analysis, 1-D Wavelet Coefficient Method and Partial Correlation Method have been analyzed on some Wechsler Adult Intelligence Scale-Revised parameters such as School Education, Gender, Age, Performance Information Verbal and Full Scale Intelligence Quotient. In particular, we will show that gender variable has a negative but a significant role on age and Performance Information Verbal factors. The age parameters also has a significant relation in its role on Performance Information Verbal and Full Scale Intelligence Quotient change.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Anderson, J. C., and Gerbing, D. W., Structural equation modeling in practice: A review and recommended two step approach. Psychol. Bull. 103:411–423, 1998.

    Article  Google Scholar 

  2. Gordon, E. T., and Benson, N., Matters of consequence: An empirical investigation of the WAIS-III and WAIS-IV and implications for addressing the atkins intelligence criterion. Journal of Forensic Psychology Practice 13:2748, 2013.

    Google Scholar 

  3. Beaujean, A., and Osterlind, S. J., Using item response theory to assess the Flynn effect in the national longitudinal study of youth 79 children and young adults data. Intelligence 36:455–463, 2008.

    Article  Google Scholar 

  4. Brand, C. R., The g factor: General intelligence and its implications: Wiley 1996.

  5. Lynn, R., and Dai, X. Y., Sex differences on the chinese standardization sample of the WAIS R. J. Genet. Psychol. 154(4):459–463, 1993.

    Article  CAS  PubMed  Google Scholar 

  6. Weschler, D., WAIS-R Manual: Wechsler Adult Intelligence Scale-Revised. The Psy- chological Corporation: 1981.

  7. Adams, R. L., Smigielski, J., and Jenkins, R. L., Development of Satz-Mogel short form of the WAIS-R. Journal of Consulting and Clinical Psychological Assessment 6:5–7, 1994.

  8. Athanasou, J. A., Patterns of performance on the verbal and performance subtests of the Wechsler adult intelligence scale-revised: Some Australian data. J. Clin. Psychol. 49:102–108, 1993.

    Article  CAS  PubMed  Google Scholar 

  9. Atkinson, L., Some tables for statistically based interpretation of WAIS-R factor scores. Psychol. Assess. 3: 288–291, 1991.

    Article  Google Scholar 

  10. Matarazzo, J. D., Wechslers Measurement and Appraisal of Adult Intelligence: Williams & Witkins 1972.

  11. Tulsani, H., and Gupta, R., 1-D Signal denoising using wavelets based optimization of polynomial threshold function, 3rd International Conference on Reliability, Infocom Technologies and Optimization (ICRITO’2014), IEEE, 2014. doi:10.1109/ICRITO.2014.7014720

  12. Karaca, Y., and Aslan, Z., Wavelet analysis of anxiety and mathematics. Indian J. Ind. Appl. Math. 4(2): 118–130, 2013.

    Article  Google Scholar 

  13. Lee, D. T. L., and Yamamoto A., Wavelet analysis: Theory and applications. Hewlett Packard Journal 45(6):44– 52, 1994.

    Google Scholar 

  14. Melissa, A. S., Raghuraj, K. R., Lakshminarayanan, S., Partial correlation metric based classifier for food product characterization. J. Food Eng. 90(2):146–152, 2009.

    Article  Google Scholar 

  15. Sangita, Z.-C., Parvatham, V., and Krishna Mohan, B., Assessment of Distortion in Watermarked Geospatial Vector Data Using Different Wavelets, Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India, 2015.

  16. Ariel M. A., An empirical investigation of partial effect sizes in meta-analysis of correlational data. J. Gen. Psychol. 141:1:47–64, 2014.

    Google Scholar 

  17. Perez-Villalon G., and Portal A., Computation of wavelet coeffcients from average samples. J. Comput. Appl. Math. 248(1):118–130, 2013.

    Article  Google Scholar 

  18. Zhang, Y., Peng, B., Wang, S., et al., Image processing methods to elucidate spatial characteristics of retinal microglia after optic nerve transection. Sci. Rep. 6:21816, 2016.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Wang, S., Zhang, Y., Yang, X., et al., Pathological brain detection by a novel image feature - fractional fourier entropy. Entropy 17(12):8278–8296, 2015.

    Article  Google Scholar 

  20. Wang S., Zhang Y., and Liu G., et al., Detection of Alzheimers disease by three-dimensional displacement field estimation in structural magnetic resonance imaging. Journal of Alzheimer’s Disease 50(1):233–248, 2016.

  21. Shahvar M. B., Badounak N. D., Kharrat R., A new approach for compressional slowness modeling using wavelet coefficients. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 36:19:2106-2112, 2014.

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Prof. Diana Gallettas, her team at the Unit of Psychiatry of Department of Clinique Neuroscience, Surgery of Psychodiagnostic of the “Federico II” University of Naples for their contribution regarding the sharing of data on mental functions. The authors would also like to express their appreciation to the patients handled as subjects in the study.

Author information

Authors and Affiliations

  1. Engineering School (DEIM), University of Tuscia (Università degli Studi della Tuscia), 01100, Viterbo, Italy

    Y. Karaca & C. Cattani

  2. Computer Engineering Department, Istanbul Aydın University, 34295, Istanbul, Turkey

    Z. Aslan

  3. Department of Neuroscience, University of Naples “Federico II”, Via Pansini, 80131, Naples, Italy

    D. Galletta

  4. School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu, 210023, China

    Y. Zhang

Authors
  1. Y. Karaca
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Z. Aslan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. C. Cattani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. D. Galletta
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Y. Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Y. Karaca.

Additional information

This article is part of the Topical Collection on Patient Facing Systems

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karaca, Y., Aslan, Z., Cattani, C. et al. Rank Determination of Mental Functions by 1D Wavelets and Partial Correlation. J Med Syst 41, 2 (2017). https://doi.org/10.1007/s10916-016-0606-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-016-0606-2

Keywords

Search

Navigation