Analysis of Resting State EEG Signals of Adults with Attention-Deficit Hyperactivity Disorder

  • Simranjit Kaur
  • Sukhwinder SinghEmail author
  • Priti Arun
  • Damanjeet Kaur
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 897)


Electroencephalography (EEG) has emerged as a valuable tool to understand the neurophysiology of Attention-Deficit Hyperactivity Disorder (ADHD) brain. The purpose of this work is to examine whether linear and nonlinear electrophysiological measures of adults with ADHD differ from control group during a rest state. To verify this, in the present study, EEG signals of 23 adults with ADHD and 27 control adults are recorded during 3 min eyes-open and eyes-closed conditions. Linear features are extracted from EEG epochs which include power spectra of delta, theta, alpha, beta, and gamma frequency bands. Nonlinear features are measured in terms of entropies to unveil signal complexity. Linear analysis showed that the ADHD group has increased power for slow waves and reduced power for fast waves. Nonlinear analysis results in significant reduction in approximate entropy, sample entropy, and Shannon entropy of the ADHD adults in comparison to control adults.


ADHD Adults EEG Entropy Rest state 


  1. 1.
    Polanczyk, G., de Lima, M.S., Horta, B.L., Biederman, J., Rohde, L.A.: The worldwide prevalence of ADHD: a systematic review and metaregression analysis. Am. J. Psychiatry 164(6), 942–948 (2007)CrossRefGoogle Scholar
  2. 2.
    Wender, P.H., Wolf, L.E., Wasserstein, J.: Adults with ADHD. Ann. N. Y. Acad. Sci. 931(1), 1–16 (2001)CrossRefGoogle Scholar
  3. 3.
    Simon, V., Czobor, P., Bálint, S., Mészáros, Á., Bitter, I.: Prevalence and correlates of adult attention-deficit hyperactivity disorder: meta-analysis. Br. J. Psychiatry 194(3), 204–211 (2009)CrossRefGoogle Scholar
  4. 4.
    Fayyad, J., De Graaf, R., Kessler, R., Alonso, J., Angermeyer, M., Demyttenaere, K., Lépine, J.P.: Cross-national prevalence and correlates of adult attention-deficit hyperactivity disorder. Br. J. Psychiatry 190(5), 402–409 (2007)CrossRefGoogle Scholar
  5. 5.
    Kessler, R.C., Adler, L., Barkley, R., Biederman, J., Conners, C.K., Demler, O., Spencer, T.: The prevalence and correlates of adult ADHD in the United States: results from the National Comorbidity Survey Replication. Am. J. Psychiatry 163(4), 716–723 (2006)CrossRefGoogle Scholar
  6. 6.
    Rabiner, D.L., Anastopoulos, A.D., Costello, J., Hoyle, R.H., Swartzwelder, H.S.: Adjustment to college in students with ADHD. J. Atten. Disord. 11(6), 689–699 (2008)CrossRefGoogle Scholar
  7. 7.
    Shaw-Zirt, B., Popali-Lehane, L., Chaplin, W., Bergman, A.: Adjustment, social skills, and self-esteem in college students with symptoms of ADHD. J. Atten. Disord. 8(3), 109–120 (2005)CrossRefGoogle Scholar
  8. 8.
    Barry, R.J., Clarke, A.R., Johnstone, S.J.: A review of electrophysiology in attention-deficit/hyperactivity disorder: I. Qualitative and quantitative electroencephalography. Clin. Neurophysiol. 114(2), 171–183 (2003)CrossRefGoogle Scholar
  9. 9.
    Snyder, S.M., Hall, J.R.: A meta-analysis of quantitative EEG power associated with attention-deficit hyperactivity disorder. J. Clin. Neurophysiol. 23(5), 441–456 (2006)CrossRefGoogle Scholar
  10. 10.
    Arns, M., Conners, C.K., Kraemer, H.C.: A decade of EEG theta/beta ratio research in ADHD: a meta-analysis. J. Atten. Disord. 17(5), 374–383 (2013)CrossRefGoogle Scholar
  11. 11.
    Hobbs, M.J., Clarke, A.R., Barry, R.J., McCarthy, R., Selikowitz, M.: EEG abnormalities in adolescent males with AD/HD. Clin. Neurophysiol. 118(2), 363–371 (2007)CrossRefGoogle Scholar
  12. 12.
    Bresnahan, S.M., Barry, R.J., Clarke, A.R., Johnstone, S.J.: Quantitative EEG analysis in dexamphetamine-responsive adults with attention-deficit/hyperactivity disorder. Psychiatry Res. 141(2), 151–159 (2006)CrossRefGoogle Scholar
  13. 13.
    Koehler, S., Lauer, P., Schreppel, T., Jacob, C., Heine, M., Boreatti-Hümmer, A., Herrmann, M.J.: Increased EEG power density in alpha and theta bands in adult ADHD patients. J. Neural Transm. 116(1), 97–104 (2009)CrossRefGoogle Scholar
  14. 14.
    Clarke, A.R., Barry, R.J., Heaven, P.C., McCarthy, R., Selikowitz, M., Byrne, M.K.: EEG in adults with attention-deficit/hyperactivity disorder. Int. J. Psychophysiol. 70(3), 176–183 (2008)CrossRefGoogle Scholar
  15. 15.
    Woltering, S., Jung, J., Liu, Z., Tannock, R.: Resting state EEG oscillatory power differences in ADHD college students and their peers. Behav. Brain Funct. 8(1), 60–68 (2012)CrossRefGoogle Scholar
  16. 16.
    Markovska-Simoska, S., Pop-Jordanova, N.: Quantitative EEG in children and adults with attention deficit hyperactivity disorder: comparison of absolute and relative power spectra and theta/beta ratio. Clin. EEG Neurosci. 48(1), 20–32 (2017)CrossRefGoogle Scholar
  17. 17.
    Acharya, U.R., Sree, S.V., Swapna, G., Martis, R.J., Suri, J.S.: Automated EEG analysis of epilepsy: a review. Knowl.-Based Syst. 45, 147–165 (2013)CrossRefGoogle Scholar
  18. 18.
    Al-Kadi, M.I., Reaz, M.B.I., Ali, M.A.M.: Evolution of electroencephalogram signal analysis techniques during anesthesia. Sensors 13(5), 6605–6635 (2013)CrossRefGoogle Scholar
  19. 19.
    Acharya, R., Faust, O., Kannathal, N., Chua, T., Laxminarayan, S.: Non-linear analysis of EEG signals at various sleep stages. Comput. Methods Programs Biomed. 80(1), 37–45 (2005)CrossRefGoogle Scholar
  20. 20.
    Artameeyanant, P., Chiracharit, W., Chamnongthai, K.: Spike and epileptic seizure detection using wavelet packet transform based on approximate entropy and energy with artificial neural network. In: Biomedical Engineering International Conference (BMEiCON), 2012, pp. 1–5. IEEE (2012)Google Scholar
  21. 21.
    Ward, M.F.: The Wender Utah Rating Scale: An aid in the retrospective diagnosis of childhood attention deficit hyperactivity disorder. Am. J. Psychiatry 150, 885–990 (1993)CrossRefGoogle Scholar
  22. 22.
    Kessler, R.C., Adler, L., Ames, M., Demler, O., Faraone, S., Hiripi, E.V.A., Ustun, T.B.: The World Health Organization Adult ADHD Self-Report Scale (ASRS): a short screening scale for use in the general population. Psychol. Med. 35(2), 245–256 (2005)CrossRefGoogle Scholar
  23. 23.
    Jasper, H.: Report of the committee on methods of clinical examination in electroencephalography. Electroencephalogr. Clin. Neurophysiol. 10, 370–375 (1958)CrossRefGoogle Scholar
  24. 24.
    Daly, I., Scherer, R., Billinger, M., Müller-Putz, G.: FORCe: Fully Online and automated artifact Removal for brain-Computer interfacing. IEEE Trans. Neural Syst. Rehabil. Eng. 23(5), 725–736 (2015)CrossRefGoogle Scholar
  25. 25.
    Loo, S.K., Makeig, S.: Clinical utility of EEG in attention-deficit/hyperactivity disorder: a research update. Neurotherapeutics 9(3), 569–587 (2012)CrossRefGoogle Scholar
  26. 26.
    Wali, M.K., Murugappan, M., Ahmmad, B.: Wavelet packet transform based driver distraction level classification using EEG. Math. Problems in Eng. 2013 (2013)Google Scholar
  27. 27.
    Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278(6), H2039–H2049 (2000)CrossRefGoogle Scholar
  28. 28.
    Shannon, C.E.: A mathematical theory of communication. ACM SIGMOBILE Mob. Comput. Commun. Rev. 5(1), 3–55 (2001)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Bandt, C., Pompe, B.: Permutation entropy: a natural complexity measure for time series. Phys. Rev. Lett. 88(17), 174102–174106 (2002)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Simranjit Kaur
    • 1
  • Sukhwinder Singh
    • 1
    Email author
  • Priti Arun
    • 2
  • Damanjeet Kaur
    • 1
  1. 1.University Institute of Engineering and Technology, Panjab UniversityChandigarhIndia
  2. 2.Government Medical College and HospitalChandigarhIndia

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