Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon openaccess Fuzzy-entropy threshold based on a complex wavelet denoising technique to diagnose Alzheimer disease

  • Author(s): Prinza Lazar 1 ; Rajeesh Jayapathy 2 ; Jordina Torrents-Barrena 3 ; Beena Mol 4 ; Mohanalin 5 ; Domenec Puig 3
    • View affiliations
    • Affiliations: 1: Department of Electronics and Communication Engineering, PJCE, Anna University, Chennai, India ;
      2: Department of Electronics and Communication Engineering, PJCE, Nagercoil, India ;
      3: Department of Computer Engineering and Mathematics, University Rovira i Virgili, Spain ;
      4: Department of Civil Engineering, NGCE, Manjalumoodu, Kanyakumari, India ;
      5: Department of Electrical and Electronics Engineering, LMCST, Trivandrum, India
  • Source: Volume 3, Issue 3, September 2016, p. 230 – 238
    DOI:  10.1049/htl.2016.0022 , Online ISSN 2053-3713
© The Institution of Engineering and Technology
Received 08/04/2016, Accepted 15/06/2016, Revised 14/06/2016, Published 01/07/2016

The presence of irregularities in electroencephalographic (EEG) signals entails complexities during the Alzheimer's disease (AD) diagnosis. In addition, the uncertainty presented on EEG raises major issues in the improvement of the classification rate. The multi-resolution analysis through an optimum threshold will likely achieve better results in distinguishing AD and normal EEG signals. Hence, a fuzzy-entropy concept defined in a complex multi-resolution wavelet has been proposed to obtain the most appropriate threshold. First, the complex coefficients are fuzzified using a Gaussian membership function. Afterwards, the ability of the proposed fuzzy-entropy threshold has been compared with traditional thresholds in complex wavelet domain. Experimental results show that the authors’ methodology produces a higher signal-to-noise ratio and a lower root-mean-square error than traditional approaches. Moreover, a neural network scheme is performed along several features to classify AD from normal EEG signals obtaining a specificity of 87.5%.

Inspec keywords: mean square error methods; wavelet transforms; entropy; wavelet neural nets; fuzzy systems; signal denoising; medical signal processing; signal classification; electroencephalography; diseases

Other keywords: uncertainty; irregularities; lower root-mean-square error; Gaussian membership function; complex wavelet denoising technique; multiresolution wavelet; Alzheimer disease diagnosis; neural network scheme; signal-to-noise ratio; AD EEG signals; multiresolution analysis; electroencephalographic signals; fuzzy-entropy threshold; optimum threshold; classification rate

Subjects: Neural computing techniques; Signal processing and detection; Digital signal processing; Electrodiagnostics and other electrical measurement techniques; Biology and medical computing; Bioelectric signals; Electrical activity in neurophysiological processes

References

    1. 1)
    2. 2)
    3. 3)
      • 15. Adam, I.: ‘Complex wavelet transform: Application to denoising’, Ph.D. thesis, 2010.
    4. 4)
      • 18. Becerikli, Y., Karan, T.M.: ‘A new fuzzy approach for edge detection’. IWANN 2005, (LNCS 3512o|), pp. 943–951.
    5. 5)
      • 20. Mathew, B.a.: ‘Entropy of electroencephalogram (EEG) signals changes with sleep’, Master's thesis, State university of Kentucky, 2006.
    6. 6)
    7. 7)
    8. 8)
    9. 9)
      • 21. Wang, Y.: ‘Wavelet transform based feature extraction for ultrasonic flaw signal classification’, J. Comput., 2014, 9, (3), pp. 725–732.
    10. 10)
      • 12. Shukla, P.D.: ‘Complex wavelet transforms and their applications’, Ph.D. thesis, The University of Strathclyde in Glasgow, 2003.
    11. 11)
      • 34. Zhao, Y., He, L.: ‘Deep learning in the EEG diagnosis of Alzheimer's disease’. ACCV 2014 Workshop, 2014, vol. 9008, pp. 340–353.
    12. 12)
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
    22. 22)
    23. 23)
    24. 24)
    25. 25)
    26. 26)
    27. 27)
    28. 28)
    29. 29)
      • 2. Prince, M., Bryce, R., Ferri, C.: ‘World Alzheimer report 2011: The benefits of early diagnosis and intervention’, Alzheimer's Disease International (ADI), 2011.
    30. 30)
    31. 31)
    32. 32)
      • 6. Salwani, M.D.: ‘Comparison of few wavelets to filter ocular artifacts in EEG using lifting wavelet transform’, TENCON IEEE Region, 2005, 10, pp. 1–6.
    33. 33)
    34. 34)
    35. 35)
      • 25. Dangeti, S.: ‘Denoising Techniques: A comparison’. Master's thesis, Louisiana State University, Agricultural and Mechanical College, 2003.
    36. 36)
      • 3. World Alzheimer Report. Alzheimer's Disease International, 2015.
http://iet.metastore.ingenta.com/content/journals/10.1049/htl.2016.0022
Loading

Related content

content/journals/10.1049/htl.2016.0022
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address