Skip to main content
SpringerLink
Log in

Identification of attention deficit hyperactivity disorder with deep learning model

  • Scientific Paper
  • Published:
Physical and Engineering Sciences in Medicine Aims and scope Submit manuscript

Abstract

This article explores the detection of Attention Deficit Hyperactivity Disorder, a neurobehavioral disorder, from electroencephalography signals. Due to the unstable behavior of electroencephalography signals caused by complex neuronal activity in the brain, frequency analysis methods are required to extract the hidden patterns. In this study, the feature extraction was performed with the Multitaper and Multivariate Variational Mode Decomposition methods. Then, these features were analyzed with the neighborhood component analysis and the features that contribute effectively to the classification were selected. The deep learning model including the convolution, pooling, and bidirectional long short term cell and fully connected layer was trained with the selected features. The trained model could effectively classify the subjects with Attention Deficit Hyperactivity Disorder with a deep learning model, support vector machines and linear discriminant analysis. The experiments were validated with an Attention Deficit Hyperactivity Disorder open access dataset (https://doi.org/10.21227/rzfh-zn36). In validation, the deep learning model was able to classify 1210 test samples (600 subjects in the control group as Normal and 610 subjects in the ADHD group as ADHD) in 0.1 s with an accuracy of 95.54%. This accuracy rate is quite high compared to the Linear Discriminant Analysis (76.38%) and Support Vector Machines (81.69%). Experimental results showed that the proposed approach can innovatively classify Attention Deficit Hyperactivity Disorder subjects from the Control group effectively.

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
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Homri I, Yacoub S (2019) A hybrid cascade method for EEG classification. Pattern Anal Appl 22(4):1505–1516

    Article  Google Scholar 

  2. Dubreuil-Vall L, Ruffini G, Camprodon JA (2020) Deep learning convolutional neural networks discriminate adult ADHD from healthy individuals on the basis of event-related spectral EEG. Front Neurosci. https://doi.org/10.3389/fnins.2020.00251

    Article  PubMed  PubMed Central  Google Scholar 

  3. Taylor, E. (1994). Syndromes of attention deficit and overactivity. Child and adolescent psychiatry: Modern approaches.

  4. Amado-Caballero P, Casaseca-de-la-Higuera P, Alberola-Lopez S, Andres-de-Llano JM, Lopez-Villalobos JA, Garmendia-Leiza JR, Alberola-Lopez C (2020) Objective ADHD diagnosis using Convolutional Neural Networks over Daily-Life Activity Records. IEEE J Biomed Health Inform. https://doi.org/10.1109/JBHI.2020.2964072

    Article  PubMed  Google Scholar 

  5. Alba G, Pereda E, Mañas S, Méndez LD, González A, González JJ (2015) Electroencephalography signatures of attention-deficit/hyperactivity disorder: clinical utility. Neuropsychiatr Dis Treat 11:2755

    PubMed  PubMed Central  Google Scholar 

  6. Kompatsiari K, Candrian G, Mueller A (2016) Test-retest reliability of ERP components: a short-term replication of a visual Go/NoGo task in ADHD subjects. Neurosci Lett 617:166–172

    Article  CAS  PubMed  Google Scholar 

  7. Marcano JL, Bell MA, Beex AL (2018) Classification of ADHD and non-ADHD subjects using a universal background model. Biomed Signal Process Control 39:204–212

    Article  PubMed  Google Scholar 

  8. Tosun M (2021) Effects of spectral features of EEG signals recorded with different channels and recording statuses on ADHD classification with deep learning. Phys Eng Sci Med. https://doi.org/10.1007/s13246-021-01018-x

    Article  PubMed  Google Scholar 

  9. McAuliffe D, Hirabayashi K, Adamek JH, Luo Y, Crocetti D, Pillai AS et al (2020) Increased mirror overflow movements in ADHD are associated with altered EEG alpha/beta band desynchronization. Eur J Neurosci 51(8):1815–1826

    Article  PubMed  Google Scholar 

  10. Aydemir E, Tuncer T, Dogan S (2020) A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method. Med Hypotheses 134:109519

    Article  PubMed  Google Scholar 

  11. Oliveira GH, Coutinho LR, da Silva JC, Pinto IJ, Ferreira JM, Silva FJ et al (2020) Multitaper-based method for automatic k-complex detection in human sleep EEG. Expert Syst Appl 151:113331

    Article  Google Scholar 

  12. Yang J, Li W, Wang S, Lu J, Zou L (2016) Classification of children with attention deficit hyperactivity disorder using PCA and K-nearest neighbors during interference control task. In: Wang R, Pan X (eds) Advances in cognitive neurodynamics (V). Springer, Singapore, pp 447–453

    Chapter  Google Scholar 

  13. Khoshnoud S, Nazari MA, Shamsi M (2018) Functional brain dynamic analysis of ADHD and control children using nonlinear dynamical features of EEG signals. J Integr Neurosci 17(1):17–30

    Article  Google Scholar 

  14. Chen H, Chen W, Song Y, Sun L, Li X (2019) EEG characteristics of children with attention-deficit/hyperactivity disorder. Neuroscience 406:444–456

    Article  CAS  PubMed  Google Scholar 

  15. Jahanshahloo HR, Shamsi M, Ghasemi E, Kouhi A (2017) Automated and ERP-based diagnosis of attention-deficit hyperactivity disorder in children. J Medical Signals Sens 7(1):26

    Article  Google Scholar 

  16. Mueller A, Candrian G, Grane VA, Kropotov JD, Ponomarev VA, Baschera GM (2011) Discriminating between ADHD adults and controls using independent ERP components and a support vector machine: a validation study. Nonlinear Biomed Phys 5(1):5

    Article  PubMed  PubMed Central  Google Scholar 

  17. De Dea F, Ajčević M, Stecca M, Zanus C, Carrozzi M, Cuzzocrea A, Accardo A (2019) A big-data-analytics framework for supporting classification of ADHD and healthy children via principal component analysis of EEG sleep spindles power spectra. Procedia Comput Sci 159:1584–1590

    Article  Google Scholar 

  18. Altınkaynak M, Dolu N, Güven A, Pektaş F, Özmen S, Demirci E, İzzetoğlu M (2020) Diagnosis of attention deficit hyperactivity disorder with combined time and frequency features. Biocybern Biomed Eng. https://doi.org/10.1016/j.bbe.2020.04.006

    Article  Google Scholar 

  19. Khaleghi A, Sheikhani A, Mohammadi MR, Nasrabadi AM, Vand SR, Zarafshan H, Moeini M (2015) EEG classification of adolescents with type I and type II of bipolar disorder. Australas Phys Eng Sci Med 38(4):551–559

    Article  PubMed  Google Scholar 

  20. Boroujeni YK, Rastegari AA, Khodadadi H (2019) Diagnosis of attention deficit hyperactivity disorder using non-linear analysis of the EEG signal. IET Syst Biol 13(5):260–266

    Article  PubMed  PubMed Central  Google Scholar 

  21. Chen H, Song Y, Li X (2019) A deep learning framework for identifying children with ADHD using an EEG-based brain network. Neurocomputing 356:83–96

    Article  Google Scholar 

  22. Nasrabadi AM, Allahverdy A, Samavati M, Mohammadi MR (2020) EEG data for ADHD/Control children. IEEE Dataport. https://doi.org/10.21227/rzfh-zn36

    Article  Google Scholar 

  23. Chang L, Wang R, Zhang Y (2022) Decoding SSVEP patterns from EEG via multivariate variational mode decomposition-informed canonical correlation analysis. Biomed Signal Process Control 71:103209

    Article  Google Scholar 

  24. Gavas R, Jaiswal D, Chatterjee D, Viraraghavan V, Ramakrishnan RK (2020) Multivariate Variational Mode Decomposition based approach for Blink Removal from EEG Signal. In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (pp. 1–6).

  25. Wieczorek MA, Simons FJ (2007) Minimum-variance Multitaper spectral estimation on the sphere. J Fourier Anal Appl 13(6):665–692

    Article  Google Scholar 

  26. Goldberger J, Hinton GE, Roweis ST, Salakhutdinov RR (2005) Neighbourhood components analysis. In Advances in neural information processing systems (pp. 513–520).

  27. McLachlan GJ (2004) Discriminant analysis and statistical pattern recognition (Vol. 544). Wiley.

  28. Ben-Hur A, Horn D, Siegelmann HT, Vapnik V (2001) Support vector clustering. J Mach Learn Res 2:125–137

    Google Scholar 

  29. Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw 18(5–6):602–610

    Article  PubMed  Google Scholar 

  30. Fouladvand S, Hankosky ER, Henderson DW, Bush H, Chen J, Dwoskin L P, et al. (2018) Predicting Substance Use Disorder in ADHD Patients using Long-Short Term Memory Model. In 2018 IEEE International Conference on Healthcare Informatics Workshop (ICHI-W) (pp. 49–50).

  31. Mohammadi MR, Khaleghi A, Nasrabadi AM, Rafieivand S, Begol M, Zarafshan H (2016) EEG classification of ADHD and normal children using non-linear features and neural network. Biomed Eng Lett 6(2):66–73

    Article  Google Scholar 

  32. Abbas AK, Azemi G, Amiri S, Ravanshadi S, Omidvarnia A (2021) Effective connectivity in brain networks estimated using EEG signals are altered in children with attention deficit hyperactivity disorder. Comput Biol Med 134:104515

    Article  PubMed  Google Scholar 

Download references

Funding

This work was not funded by any organization.

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Simav Technology Faculty, Kutahya Dumlupinar University, 43500, Kutahya, Turkey

    Ömer Kasim

Authors
  1. Ömer Kasim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ömer Kasim.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kasim, Ö. Identification of attention deficit hyperactivity disorder with deep learning model. Phys Eng Sci Med 46, 1081–1090 (2023). https://doi.org/10.1007/s13246-023-01275-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13246-023-01275-y

Keywords

Search

Navigation