Skip to main content

Detection of Abnormalities for Diagnosing of Children with Autism Disorders Using of Quantitative Electroencephalography Analysis

Journal of Medical Systems volume 36, pages 957–963 (2012)Cite this article

Abstract

Quantitative electroencephalography (qEEG) has been used as a tool for neurophysiologic diagnostic. We used spectrogram and coherence values for evaluating qEEG in 17 children (13 boys and 4 girls aged between 6 and 11) with autism disorders (ASD) and 11 control children (7 boys and 4 girls with the same age range). Evaluation of qEEG with statistical analysis demonstrated that alpha frequency band (8–13 Hz) had the best distinction level of 96.4% in relaxed eye-opened condition using spectrogram criteria. The ASD group had significant lower spectrogram criteria values in left brain hemisphere, (p < 0.01) at F3 and T3 electrodes and (p < 0.05) at FP1, F7, C3, Cz and T5 electrodes. Coherence values at 171 pairs of EEG electrodes indicated that there are more abnormalities with higher values in the connectivity of temporal lobes with other lobes in gamma frequency band (36–44 Hz).

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

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

Fig. 1
Fig. 2
Fig. 3

References

  1. Baird, G., Charman, T., Cox, A., Baron-Cohen, S., Swettenham, J., Wheelwright, S., et al., Screening and Surveillance for autism and pervasive developmental disorders. Arch. Dis. Child. 84:468–475, 2001.

    Article  Google Scholar 

  2. Charman, T., and Baird, G., Practitioner review: Diagnosis of autism spectrum disorder in 2- and 3-year-old children. J. Child Psychol. Psychiatry 43:289–305, 2002.

    Article  Google Scholar 

  3. Rogers, S., Dignosis of autism before the age of 3. International Review of Mental Retardation 23:1–31, 2001.

    Article  Google Scholar 

  4. Akhondzadeh, S., Erfani, S., Mohammadi, M. R., Taheri-Doost, M., Amini, H., Gudarzi, S. S., and Yasamyet, M. T., Cyproheptadine in the treatment of autistic disorder: A double-blind placebo-controlled trial. J. Clin. Pharm. Ther. 29:145–150, 2004.

    Article  Google Scholar 

  5. Blaxill, M. F., What’s going on? The question of time trends in autism. Public Health Rep. 119:536–551, 2004.

    Article  Google Scholar 

  6. Coben, R., Clarke, A. R., Hudspeth, W., and Barry, R. J., EEG power and coherence in autistic spectrum disorder. Clin. Neurophysiol. 119:1002–1009, 2008.

    Article  Google Scholar 

  7. Fombonne, E., The epidemiology of autism: A review. Psychol. Med. 29:769–786, 1999.

    Article  Google Scholar 

  8. Sarbadhikari, S. N., and Chakrabarty, K., Chaos in the brain: A short review alluding to epilepsy, depression, exercise and lateralization. Med. Eng. Phys. 23:447–457, 2001.

    Article  Google Scholar 

  9. Jeong, J., Chase, J. H., Kim, S. Y., and Han, S. H., Nonlinear dynamics analysis of EEG in patients with Alzheimer’s disease and vascular dementia. J. Clin. Neurophysiol. 18:58–67, 2001.

    Article  Google Scholar 

  10. Hudetz, A. G., Effects of volatile anesthetics on interhemisphemispheric EEG cross-approximate entropy in the rat. Brain Res. 954:123–131, 2002.

    Article  Google Scholar 

  11. Tong, S., Bezerianos, A., Paul, J., Zhu, Y., and Thakor, N. V., Nonextensive entropy measure of EEG following brain injury from cardiac arrest. Physica, A 305:619–628, 2002.

    Article  MATH  Google Scholar 

  12. Chez, M. G., Chang, M., Krasne, V., Coughlan, C., Kominsky, M., and Schwartz, A., Frequency of epileptiform EEG abnormalities in a sequential screening of autistic patients with no known clinical epilepsy from 1996 to 2005. Epilepsy Behav. 8:267–271, 2006.

    Article  Google Scholar 

  13. Rossi, P. G., Parmeggiani, A., Bach, V., Santucci, M., and Visconti, P., EEG features and epilepsy in patients with autism. Brain Dev. 17:169–174, 1995.

    Article  Google Scholar 

  14. Bashina, V. M., Gorbachevskaia, N. L., Simashkova, N. V., Iznak, A. F., Kozhushko, L. F., and Iakupova, L. P., The clinical, neurophysiologial and differential diagnostic aspects in a study of severe forms of early childhood autism. Zh Nevropatol Psikhiatr Im S S Korsakova 94:68–71, 1994.

    Google Scholar 

  15. Orekhova, E. V., Stroganova, T. A., Nygren, G., Tsetlin, M. M., Posikera, I. N., Gillberg, C., and Elam, M., Excess of High Frequency Electroencephalogram oscillation in boys with autism. Biol. Psychiatry 62:1022–1029, 2007.

    Article  Google Scholar 

  16. Dawson, G., Klinger, L. G., Panagiotides, H., Lewy, A., and Castello, P., Subgroups of autistic children based on social behavior display distinct patterns of brain activity. J. Abnorm. Child Psychol. 23:569–583, 1995.

    Article  Google Scholar 

  17. Stroganova, T. A., Nygren, G., Tsetlin, M. M., Posikera, I. N., Gillberg, C., Elam, M., and Orekhova, E. V., Abnormal EEG lateralization in boys with autism. Clin. Neurophysiol. 118:1842–1854, 2007.

    Article  Google Scholar 

  18. Oberman, L. M., Hubbard, E. M., McCleery, J. P., Altschuler, E. L., Ramachandran, V. S., and Pineda, J. A., EEG evidence for mirror neuron dysfunction in autism spectrum disorders. Cogn. Brain Res. 24(2):190–198, 2005.

    Article  Google Scholar 

  19. American Psychiatric Association, Task force on DSM-IV Diagnostic and statistical manual of mental disorders, DSM-IV-IR 4th ed. American Psychiatric Association, Washington, DC, 2000.

  20. Wechsler, D., Whechsler Intelligence Scale for children-Third Edition (WISC-III). The Psychological Corporation, San Antonio, 1991.

    Google Scholar 

  21. Oldfield, R. C., The assessment and analysis of handedness the Edinburgh inventory. Neuropsychologia 9:97–113, 1971.

    Article  Google Scholar 

  22. Jasper, H. H., Report of committee on methods of clinical examination in electroencephalography. Electroencephalogr. Clin. Neurophysiol. 10:370–375, 1958.

    Article  Google Scholar 

  23. Herbert, M. R., Ziegler, D. A., Makris, N., Filipek, P. A., Kemper, T. L., Normandin, J. J., et al., Localization of white matter volume increase in autism and developmental language disorder. Ann. Neurol. 55:530–540, 2004.

    Article  Google Scholar 

  24. Nunez, P. L., and Srinvasan, R., Electric fields of the brain; The Neurophysics of EEG, 2nd edition. Oxford University Press, New York, 2006.

    Book  Google Scholar 

  25. Tokmakei, M., and Erdogan, N., Investigation of the arterial stiffness on renal artery Doppler sonograms. J. Med. Syst. 33:101–106, 2009.

    Article  Google Scholar 

  26. Diaz, M., Application of Fourier linear spectral analyses to the characterization of smooth muscle contractile signals. J. Biochem. Biophys. Meth. 70:803–808, 2007.

    Article  Google Scholar 

  27. Hardalac, F., Yildirin, H., and Serchatlioglu, S., Determination of carotid disease with the application of STFT and CWT methods. Comput. Biol. Med. 37:785–792, 2007.

    Article  Google Scholar 

  28. Subha, D. P., Joseph, P. K., Acharya, U. R., and Lim, C. M., EEG signal analysis: A survey. J. Med. Syst. 34:195–212, 2010.

    Google Scholar 

  29. Tauscher, J., Fischer, P., Neumeister, A., Rappelsberger, P., and Kasper, S., Low frontal electroencephalographic coherence in neuroleptic-free schizophrenic patients. Biol. Psychiatry 44:438–447, 1998.

    Article  Google Scholar 

  30. Weiss, S., and Rappelsberger, P., Long-range EEG synchronization during word encoding correlates with successful memory performance. Cogn. Brain Res. 9:299–312, 2000.

    Article  Google Scholar 

  31. Tuncel, D., Dizibuyuk, A., Kiymik, M. K., Time frequency band coherence analysis between EEG and EMG activities in fatigue duration. J. Med. Syst. 34:131–138, 2010.

    Google Scholar 

  32. Metz, C. E., Basic principles of ROC analysis. Semin. Nuc. Med. 5:283–298, 1978.

    Article  Google Scholar 

  33. Abasolo, D., Homero, R., Espino, P., Alvavez, D., and Poza, J., Entropy analysis of the EEG background activity in Alzheimer’s desease patients. Physiol. Meas. 27:241–253, 2006.

    Article  Google Scholar 

  34. Lotte, F., Congedo, M., Lecuyer, A., Lamarche, F. and Arnaldi, B., A review of classification algorithms for EEG-based brain-computer interfaces. J. Neural Eng. R1–R13, 2007.

  35. Chandana, S. R., Behen, M. E., Juhasz, C., Muzik, O., Rothermel, R. D., and Manager, T. J., Significant of abnormalities trajectory and asymmetry of cortical serotonin synthesis in autism. Int. J. Dev. Neurosci. 23:171–182, 2005.

    Article  Google Scholar 

  36. Lazarev, V. V., Pontes, A., and deAzevedo, L. C., Right hemisphere deficit in EEG photic driving reactivity in childhood autism. Int. J. Psychophysiol. 4:54–79, 2004.

    Google Scholar 

  37. Sheikhani, A., Behnam, H., Noroozian, M., Mohammadi, M. R., and Mohammadi, M., Abnormalities of quantitative electroencephalography in children with Asperger disorder in various conditions. Research in Autism Spectrum Disorders 3:538–546, 2009.

    Article  Google Scholar 

  38. Klimesch, W., and Schack, B., The functional significance of theta and upper alpha oscillations. Exp. Psychol. 52:99–108, 2005.

    Article  Google Scholar 

  39. Boddaert, N., and Zilbovicius, M., Temporal lobe dysfunction in childhood.autism. Brain 123:1838–1844, 2000.

    Article  Google Scholar 

  40. Cherkassky, V. L., Kana, R. K., Keller, T. A., and Just, M. A., Functional connectivity in a baseline resting-state network in autism. NeuroReport 17:1687–1690, 2006.

    Article  Google Scholar 

  41. Murias, M., Webb, S. J., Greenson, J., and Dawson, G., Resting state cortical connectivity reflected in EEG coherence in individuals with autism. Biol. Psychiatry 62:270–273, 2006.

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by Islamic Azad University, Science and Research branch-Tehran-Iran and Center of Research in Psychiatry and Psychology (Roozbeh Hospital) Tehran University of Medical science and Omid Iranian Children Association. We express our appreciation to Dr Gholam Reza Askarifar and Dr Kambiz Kamkari for the collection of children and review of statistical computations.

Author information

Authors and Affiliations

  1. Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Ali Sheikhani

  2. Electrical Engineering Department, Iran University of Science & Technology, Tehran, Iran

    Hamid Behnam

  3. Psychiatry and Psychology Research Centre (PPRC), Roozbeh Hospital, Tehran University of Medical Science, Tehran, Iran

    Mohammad Reza Mohammadi & Maryam Noroozian

  4. Iranian Behavioral and Psychological Center for Children and Adolescents, Tehran, Iran

    Mohammad Mohammadi

Authors
  1. Ali Sheikhani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamid Behnam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Reza Mohammadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maryam Noroozian
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohammad Mohammadi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Sheikhani.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Sheikhani, A., Behnam, H., Mohammadi, M.R. et al. Detection of Abnormalities for Diagnosing of Children with Autism Disorders Using of Quantitative Electroencephalography Analysis. J Med Syst 36, 957–963 (2012). https://doi.org/10.1007/s10916-010-9560-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-010-9560-6

Keywords

  • Autism disorders
  • Quantitative Electroencephalography (qEEG)
  • Spectrogram
  • Coherence