15
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Exploring role of prefrontal cortex region of brain in children having ADHD with machine learning: Implications and insights

, &

References

  • Ahmadi, A., Kashefi, M., Shahrokhi, H., & Nazari, M. A. (2021). Computer aided diagnosis system using deep convolutional neural networks for ADHD subtypes. Biomedical Signal Processing and Control, 63, 102227. https://doi.org/10.1016/j.bspc.2020.102227
  • Alim, A., & Imtiaz, M. H. (2023). Automatic identification of children with ADHD from EEG brain waves. Signals, 4(1), 193–205. https://doi.org/10.3390/signals4010010
  • 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. Biocybernetics and Biomedical Engineering, 40(3), 927–937. https://doi.org/10.1016/j.bbe.2020.04.006
  • Arnsten, A. F. (2009). The emerging neurobiology of attention deficit hyperactivity disorder: The key role of the prefrontal association cortex. The Journal of Pediatrics, 154(5), I–S43. https://doi.org/10.1016/j.jpeds.2009.01.018
  • Bakhtyari, M., & Mirzaei, S. (2022). ADHD detection using dynamic connectivity patterns of EEG data and ConvLSTM with attention framework. Biomedical Signal Processing and Control, 76, 103708. https://doi.org/10.1016/j.bspc.2022.103708
  • Beriha, S. S. (2018). Computer aided diagnosis system to distinguish ADHD from similar behavioral disorders. Biomedical & Pharmacology Journal, 11(2), 1135–1141.
  • Carr, P., & Madan, D. (1999). Option valuation using the fast fourier transform. The Journal of Computational Finance, 2(4), 61–73. https://doi.org/10.21314/JCF.1999.043
  • Chao, H., Zhi, H., Dong, L., & Liu, Y. (2018). Recognition of emotions using multichannel EEG data and DBN-GC-based ensemble deep learning framework. Computational Intelligence and Neuroscience, 2018, 9750904–9750911. https://doi.org/10.1155/2018/9750904
  • Chauhan, N., & Choi, B.-J. (2023). Regional contribution in electrophysiological-based classifications of attention deficit hyperactive disorder (ADHD) using machine learning. Computation, 11(9), 180. https://doi.org/10.3390/computation11090180
  • Danielson, M. L., Bitsko, R. H., Ghandour, R. M., Holbrook, J. R., Kogan, M. D., & Blumberg, S. J. (2018). Prevalence of parentreported ADHD diagnosis and associated treatment among U.S. children and adolescents, 2016. Journal of Clinical Child & Adolescent Psychology, 47(2), 199–212. https://doi.org/10.1080/15374416.2017.1417860
  • Djemili, R., Bourouba, H., & Amara Korba, M. C. (2016). Application of empirical mode decomposition and artificial neural network for the classification of normal and epileptic EEG signals. Biocybernetics and Biomedical Engineering, 36(1), 285–291. https://doi.org/10.1016/j.bbe.2015.10.006
  • Ekhlasi, A., Nasrabadi, A. M., & Mohammadi, M. R. (2021). Direction of information flow between brain regions in ADHD and healthy children based on EEG by using directed phase transfer entropy. Cognitive Neurodynamics, 15(6), 975–986. https://doi.org/10.1007/s11571-021-09680-3
  • Faraone, S. V., Banaschewski, T., Coghill, D., Zheng, Y., Biederman, J., Bellgrove, M. A., Newcorn, J. H., Gignac, M., Al Saud, N. M., Manor, I., Rohde, L. A., Yang, L., Cortese, S., Almagor, D., Stein, M. A., Albatti, T. H., Aljoudi, H. F., Alqahtani, M. M. J., Asherson, P., … Wang, Y. (2021). The world federation of ADHD international consensus statement: 208 evidence-based conclusions about the disorder. Neuroscience and Biobehavioral Reviews, 128(9), 789–818. https://doi.org/10.1016/j.neubiorev.2021.01.022
  • Flandrin, P., Rilling, G., & Goncalves, P. (2004). Empirical mode decomposition as a filter bank. IEEE Signal Processing Letters, 11(2), 112–114. https://doi.org/10.1109/LSP.2003.821662
  • Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454(1971), 903–995. https://doi.org/10.1098/rspa.1998.0193
  • Jafari, P., Ghanizadeh, A., Akhondzadeh, S., & Mohammadi, M. R. (2011). Health-related quality of life of Iranian children with attention deficit/hyperactivity disorder. Quality of Life Research, 20(1), 31–36. https://doi.org/10.1007/s11136-010-9722-5
  • Khaleghi, A., Mohammadi, M. R., Moeini, M., Zarafshan, H., & Fadaei Fooladi, M. (2019). Abnormalities of alpha activity in frontocentral region of the brain as a biomarker to diagnose adolescents with bipolar disorder. Clinical EEG and Neuroscience, 50(5), 311–318. https://doi.org/10.1177/1550059418824824
  • Lazzaro, I., Gordon, E., Li, W., Lim, C. L., Plahn, M., Whitmont, S., Clarke, S., Barry, R. J., Dosen, A., & Meares, R. (1999). Simultaneous EEG and EDA measures in adolescent attention deficit hyperactivity disorder. International Journal of Psychophysiology, 34(2), 123–134. https://doi.org/10.1016/s0167-8760(99)00068-9
  • Lenartowicz, A., & Loo, S. K. (2014). Use of EEG to diagnose ADHD. Current Psychiatry Reports, 16(11), 498. https://doi.org/10.1007/s11920-014-0498-0
  • Loh, H. W., Ooi, C. P., Barua, P. D., Palmer, E. E., Molinari, F., & Acharya, U. R. (2022). Automated detection of ADHD: Current trends and future perspective. Computers in Biology and Medicine, 146, 105525. https://doi.org/10.1016/j.compbiomed.2022.105525
  • Loh, H. W., Ooi, C. P., Seoni, S., Barua, P. D., Molinari, F., & Acharya, U. R. (2022). Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022). Computer Methods and Programs in Biomedicine, 226, 107161. https://doi.org/10.1016/j.cmpb.2022.107161
  • Maniruzzaman, M., Hasan, M. A. M., Asai, N., & Shin, J. (2023). Optimal channels and features selection based ADHD detection from EEG signal using statistical and machine learning techniques. IEEE Access, 11, 33570–33583. https://doi.org/10.1109/ACCESS.2023.3264266
  • Maniruzzaman, M., Shin, J., Hasan, M. A. M., & Yasumura, A. (2022). Efficient feature selection and machine learning based ADHD detection using EEG signal. Computers, Materials & Continua, 72(3), 5179–5195. https://doi.org/10.32604/cmc.2022.028339
  • Mensh, B. D., Werfel, J., & Seung, H. S. (2004). Combining gamma-band power with slow cortical potentials to improve single-trial classification of electroencephalographic signals. IEEE Transactions on Biomedical Engineering, 51(6), 1052–1056. https://doi.org/10.1109/TBME.2004.827081
  • Moghaddari, M., Lighvan, M. Z., & Danishvar, S. (2020). Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG. Computer Methods and Programs in Biomedicine, 197, 105738. https://doi.org/10.1016/j.cmpb.2020.105738
  • Mohammadi, G., Shoushtari, P., Molaee Ardekani, B., & Shamsollahi, M. B. (2006). Person identification by using AR model for EEG signals. Proceedings of World Academy of Science, Engineering and Technology, 11, 281–285.
  • Mohammadi, M. R., Ahmadi, N., Khaleghi, A., Mostafavi, S. A., Kamali, K., Rahgozar, M., Ahmadi, A., Hooshyari, Z., Alavi, S. S., Molavi, P., Sarraf, N., Hojjat, S. K., Mohammadzadeh, S., Amiri, S., Arman, S., Ghanizadeh, A., Ahmadipour, A., Ostovar, R., Nazari, H., … Fombonne, E. (2019). Prevalence and correlates of psychiatric disorders in a national survey of Iranian children and adolescents. Iranian Journal of Psychiatry, 14(1), 1–15.
  • Mowlem, F. D., Rosenqvist, M. A., Martin, J., Lichtenstein, P., Asherson, P., & Larsson, H. (2019). Sex differences in predicting ADHD clinical diagnosis and pharmacological treatment. European Child & Adolescent Psychiatry, 28(4), 481–489. https://doi.org/10.1007/s00787-018-1211-3
  • Nasrabadi, A. M., Allahverdy, A., Samavati, M., & Mohammadi, M. R. (2020, June 10). EEG data for ADHD/Control children. IEEE Dataport. https://doi.org/10.21227/rzfh-zn36
  • Parashar, A., Kalra, N., Singh, J., & Goyal, R. K. (2021). Machine learning based framework for classification of children with ADHD and healthy controls. Intelligent Automation & Soft Computing, 28(3), 669–682. https://doi.org/10.32604/iasc.2021.017478
  • Shen, J., Zhang, X., Wang, G., Ding, Z., & Hu, B. (2022). An improved empirical mode decomposition of electroencephalogram signals for depression detection. IEEE Transactions on Affective Computing, 13(1), 262–271. https://doi.org/10.1109/TAFFC.2019.2934412
  • Stein, M. A., Snyder, S. M., Rugino, T. A., & Hornig, M. (2016). Commentary: Objective aids for the assessment of ADHD–further clarification of what FDA approval for marketing means and why NEBA might help clinicians. A response to Arns. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 57(6), 770–771. https://doi.org/10.1111/jcpp.12534
  • Tanko, D., Barua, P. D., Dogan, S., Tuncer, T., Palmer, E., Ciaccio, E. J., & Acharya, U. R. (2022). EPSPatNet86: Eight-pointed star pattern learning network for detection ADHD disorder using EEG signals. Physiological Measurement, 43(3), 035002. https://doi.org/10.1088/1361-6579/ac59dc
  • Tosun, M. (2021). Effects of spectral features of EEG signals recorded with different channels and recording statuses on ADHD classification with deep learning. Physical and Engineering Sciences in Medicine, 44(3), 693–702. https://doi.org/10.1007/s13246-021-01018-x
  • Welch, P. (1967). The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short modified periodograms. IEEE Transactions on Audio and Electroacoustics, 15(2), 70–73. https://doi.org/10.1109/TAU.1967.1161901

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.