http://iet.metastore.ingenta.com
1887

Diagnosis of attention deficit hyperactivity disorder using non-linear analysis of the EEG signal

Diagnosis of attention deficit hyperactivity disorder using non-linear analysis of the EEG signal

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Systems Biology — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Attention deficit hyperactivity disorder (ADHD) is a common behavioural disorder that may be found in 5%–8% of the children. Early diagnosis of ADHD is crucial for treating the disease and reducing its harmful effects on education, employment, relationships, and life quality. On the other hand, non-linear analysis methods are widely applied in processing the electroencephalogram (EEG) signals. It has been proved that the brain neuronal activity and its related EEG signals have chaotic behaviour. Hence, chaotic indices can be employed to classify the EEG signals. In this study, a new approach is proposed based on the combination of some non-linear features to distinguish ADHD from normal children. Lyapunov exponent, fractal dimension, correlation dimension and sample, fuzzy and approximate entropies are the non-linear extracted features. For computing, the chaotic time series of obtained EEG in the brain frontal lobe (FP1, FP2, F3, F4, and Fz) need to be analysed. Experiments on a set of EEG signal obtained from 50 ADHD and 26 normal cases yielded a sensitivity, specificity, and accuracy of 98, 92.31, and 96.05%, respectively. The obtained accuracy provides a significant improvement in comparison to the other similar studies in identifying and classifying children with ADHD.

References

    1. 1)
      • 1. Fabiano, G.A., Pelham, W.E., Coles, E.K.: ‘A meta-analysis of behavioral treatments for attention-deficit/hyperactivity disorder’, Clin. Psychol. Rev., 2009, 29, (2), pp. 129140.
    2. 2)
      • 2. American Psychiatric AssociationDiagnostic and Statistical Manual of Mental Disorders: DSM-5’ (American Psychiatric Association, Washington, DC, 2013, 5thEdn.).
    3. 3)
      • 3. Chenxi, L., Chen, Y., Li, Y., et al: ‘Complexity analysis of brain activity in attention-deficit/hyperactivity disorder: a multiscale entropy analysis’, Brain Res. Bull., 2016, 124, pp. 1220.
    4. 4)
      • 4. Neely, K.A., Wang, P., Chennavasin, A.P., et al: ‘Deficits in inhibitory force control in young adults with ADHD’, Neuropsychologia, 2017, 99, pp. 172178.
    5. 5)
      • 5. Stroux, D., Shushakova, A., Geburek-Höfer, A.J., et al: ‘Deficient interference control during working memory updating in adults with ADHD: an event-related potential study’, Clin. Neurophysiol., 2016, 127, (1), pp. 452463.
    6. 6)
      • 6. Sadock, B.J., Sadock, V.A: ‘Kaplan and Sadock's synopsis of psychiatry: Behavioral sciences/clinical psychiatry’ (Lippincott Williams & Wilkins., US, 2011, 10th Edn.).
    7. 7)
      • 7. Barry, R.J., Clarke, A.R., Johnstone, S.J., et al: ‘Electroencephalogram theta/beta ratio and arousal in attention-deficit/hyperactivity disorder: evidence of independent processes’, J. Biol. Psychiatry., 2009, 66, (4), pp. 398401.
    8. 8)
      • 8. Jafari, P., Ghanizadeh, A., Akhondzadeh, S., et al: ‘Health-related quality of life of Iranian children with attention deficit/hyperactivity disorder’, Qual. Life Res., 2011, 20, (1), pp. 3136.
    9. 9)
      • 9. Karimu, R.Y., Azadi, S.: ‘Diagnosing the ADHD using a mixture of expert fuzzy models’, Int. J. Fuzzy Syst., 2018, 20, (4), pp. 12821296.
    10. 10)
      • 10. Ibrahim, S., Djemal, R., Alsuwailem, A.: ‘Electroencephalography (EEG) signal processing for epilepsy and autism spectrum disorder diagnosis’, Biocybernetics Biomed. Eng., 2018, 38, (1), pp. 1626.
    11. 11)
      • 11. Marcano, J.L., Bell, M.A., Beex, A.L.: ‘Classification of ADHD and non-ADHD subjects using a universal background model’, Biomed. Signal Proc. Control, 2018, 1, (39), pp. 204212.
    12. 12)
      • 12. Khoshnoud, S., Nazari, M.A., Shamsi, M.: ‘Functional brain dynamic analysis of ADHD and control children using nonlinear dynamical features of EEG signals’, J. Integr. Neurosci., 2018, 17, (1), pp. 1730.
    13. 13)
      • 13. Tenev, A., Markovska-Simoska, S., Kocarev, L., et al: ‘Machine learning approach for classification of ADHD adults’, Int. J. Psychophysiol., 2014, 93, (1), pp. 162166.
    14. 14)
      • 14. Mohammadi, M.R., Khaleghi, A., Nasrabadi, A.M., et al: ‘EEG classification of ADHD and normal children using non-linear features and neural network’, Biomed. Eng. Lett., 2016, 6, (2), pp. 6673.
    15. 15)
      • 15. Lubar, J.F.: ‘Discourse on the development of EEG diagnostics and biofeedback for attention deficit/hyperactivity disorders’, Biofeedback Self Regul., 1991, 16, (3), pp. 201225.
    16. 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., 2017, 48, (1), pp. 2032.
    17. 17)
      • 17. Oweis, R.J., Abdulhay, E.W.: ‘Seizure classification in EEG signals utilizing hilbert-huang transform’, Biomed. Eng. Online, 2011, 10, (1), pp. 3853.
    18. 18)
      • 18. Li, P., Xu, P., Zhang, R., et al: ‘L1 norm based common spatial patterns decomposition for scalp EEG BCI’, Biomed. Eng. Online, 2013, 12, (1), pp. 7789.
    19. 19)
      • 19. Sridhar, C., Bhat, S., Acharya, U.R., et al: ‘Diagnosis of attention deficit hyperactivity disorder using imaging and signal processing techniques’, Comput. Biol. Med., 2017, 88, pp. 9399.
    20. 20)
      • 20. Briggs, J.: ‘Fractals: The Patterns of Chaos: Discovering a New Aesthetic of Art, Science and Nature’, (Touchstone / Simon and Schuster, US, 1992, 1st edn.).
    21. 21)
      • 21. Bhat, S., Acharya, U.R., Adeli, H., et al: ‘Automated diagnosis of autism: in search of a mathematical marker’, Rev. Neurosci., 2014, 25, (6), pp. 851861.
    22. 22)
      • 22. Korn, H., Faure, P.: ‘Is there chaos in the brain? II.\ experimental evidence and related models’, C. R. Biol., 2003, 326, (9), pp. 787840.
    23. 23)
      • 23. Freeman, W.J.: ‘Strange attractors that govern mammalian brain dynamics shown by trajectories of electroencephalographic (EEG) potential’, IEEE Trans. Circuits Syst., 1988, 35, (7), pp. 781783.
    24. 24)
      • 24. Ghassemi, F., Moradi, M.H., Tehrani-Doost, M., et al: ‘Using non-linear features of EEG for ADHD/normal participants’ classification’, Procedia – Soc. Behav. Sci., 2012, 32, pp. 148152.
    25. 25)
      • 25. Sadatnezhad, K., Boostani, R., Ghanizadeh, A.: ‘Classification of BMD and ADHD patients using their EEG signals’, Int. J. Expert Syst. Appl., 2011, 38, (3), pp. 19561963.
    26. 26)
      • 26. Ahmadlou, M., Adeli, H., Adeli, A.: ‘Fractality and a wavelet-chaosneural network methodology for EEG-based diagnosis of autistic spectrum disorder’, J. Clin. Neurophysiol., 2010, 27, (5), pp. 328333.
    27. 27)
      • 27. Buyck, I., Wiersema, J.R.: ‘Resting electroencephalogram in attention deficit hyperactivity disorder: developmental course and diagnostic value’, Psychiatry. Res., 2014, 216, (3), pp. 391397.
    28. 28)
      • 28. Xiang, J., Li, C., Li, H., et al: ‘The detection of epileptic seizure signals based on fuzzy entropy’, J. Neurosci. Meth., 2015, 243, pp. 1825.
    29. 29)
      • 29. Chu, Y.J., Chang, C.F., Shieh, J.S., et al: ‘The potential application of multiscale entropy analysis of electroencephalography in children with neurological and neuropsychiatric disorders’, Entropy, 2017, 19, (8), p. 428.
    30. 30)
      • 30. Mann, C.A., Lubar, J.F., Zimmerman, A.W., et al: ‘Quantitative analysis of EEG in boys with attention-deficit–hyperactivity disorder: controlled study with clinical implications’, Pediatr. Neurol., 1992, 8, (1), pp. 3036.
    31. 31)
      • 31. Adeli, H., Abba, G.L.: ‘Chaos-wavelet-neural network models for automated EEG-based diagnosis of the neurological disorders’. Proc. 17th Int. Conf. on Systems, Signals and Image Processing (IWSSIP 2010), USA, 2010.
    32. 32)
      • 32. Khodadadi, H., Khaki-Sedigh, A., Ataei, M., et al: ‘Applying a modified version of lyapunov exponent for cancer diagnosis in biomedical images: the case of breast mammograms’, Multidimen. Syst. Sign Process., 2018, 29, (1), pp. 1933.
    33. 33)
      • 33. Hilborn, R.C.: ‘Chaos and nonlinear dynamics: an introduction for scientists and engineers’ (Oxford University Press, UK, 2000, 2th Edn.).
    34. 34)
      • 34. Khodadadi, H., Khaki Sedigh, A., Ataei, M., et al: ‘Nonlinear analysis of the contour boundary irregularity of skin lesion using lyapunov exponent and K-S entropy’, J. Med. Biol. Eng., 2017, 37, (3), pp. 409419.
    35. 35)
      • 35. Arab Zade, M., Khodadadi, H.: ‘Fuzzy controller design for breast cancer treatment based on fractal dimension using breast thermograms’, IET Syst. Biol., 2019, 13, (1), pp. 17.
    36. 36)
      • 36. Rodríguez-Bermúdez, G., García-Laencina, P.J.: ‘Analysis of EEG signals using nonlinear dynamics and chaos: a review’, Appl. Math. Inf. Sci., 2015, 9, (5), pp. 23092321.
    37. 37)
      • 37. Sharma, R., Pachori, R.B., Acharya, U.R.: ‘Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals’, Entropy, 2015, 17, (2), pp. 669691.
    38. 38)
      • 38. Gao, J., Hu, J., Tung, W.W.: ‘Entropy measures for biological signal analyses’, Nonlinear Dyn., 2012, 68, (3), pp. 431444.
    39. 39)
      • 39. Acharya, U.R., Sudarshan, V.K., Adeli, H., et al: ‘Computer-aided diagnosis of depression using EEG signals’, Eur. Neurol., 2015, 73, (5–6), pp. 329336.
    40. 40)
      • 40. Li, M., Liu, H., Zhu, W., et al: ‘Applying improved multiscale fuzzy entropy for feature extraction of MI-EEG’, Appl. Sci., 2017, 7, pp. 7392.
    41. 41)
      • 41. Yang, F., Soriano, J., Kubo, T., et al: ‘Application of SsVGMM to medical data-classification with novelty detection’. 39th Annual Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBC), Seogwipo, South Korea, 11 July 2017, pp. 30983101.
    42. 42)
      • 42. Williams, L.M., Hermens, D.F., Thein, T., et al: ‘Using brain-based cognitive measures to support clinical decisions in ADHD’, Pediatr. Neurol., 2010, 42, (2), pp. 118126.
    43. 43)
      • 43. Loo, S.K., Cho, A., Hale, T.S., et al: ‘Characterization of the theta to beta ratio in ADHD: identifying potential sources of heterogeneity’, J. Atten. Disord., 2013, 17, (5), pp. 384392.
    44. 44)
      • 44. Liechti, M.D., Valko, L., Muller, U.C., et al: ‘Diagnostic value of resting electroencephalogram in attention-deficit/hyperactivity disorder across the lifespan’, Brain Topogr., 2013, 26, (1), pp. 135151.
    45. 45)
      • 45. Helgadóttir, H., Gudmundsson, Ó.Ó., Baldursson, G., et al: ‘Electroencephalography as a clinical tool for diagnosing and monitoring attention deficit hyperactivity disorder: a cross-sectional study’, BMJ Open, 2015, 5, pp. 19.
    46. 46)
      • 46. Ogrim, G., Kropotov, J., Hestad, K.: ‘The quantitative EEG theta/beta ratio in attention deficit/hyperactivity disorder and normal controls: sensitivity, specificity, and behavioral correlates’, Psychiatry Res., 2012, 198, (3), pp. 482488.
    47. 47)
      • 47. Snyder, S.M., Quintana, H., Sexson, S.B., et al: ‘Multicenter validation of EEG and rating scales in identifying ADHD within a clinical sample’, Psychiatry Res., 2008, 159, (3), pp. 346358.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-syb.2018.5130
Loading

Related content

content/journals/10.1049/iet-syb.2018.5130
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address