ADHD Detection Using Machine learning Algorithms and EEG brain signals
DOI:
https://doi.org/10.32792/jeps.v13i1.237Keywords:
ADHD,, EEG,, machine learning,, K-means.Abstract
Attention deficit hyperactivity disorder (ADHD) is a behavioral problem that can last into adulthood
and affect children. Because it can show complicated brain activity, electroencephalography (EEG) plays
a key role in determining the neurophysiology of ADHD. several statistical features are extracted from
five frequency bands by using a discrete wavelet transform. The proposed system is evaluated by using
K-means-based feature selection and 5 machine learning methods (Least-square support vector machine,
k-nearest neighbor, Decision tree, and naive-Bayes classifier, support vector machine), so this system
developed using a ten-fold cross-validation strategy and showed the testing accuracy for each classifier as
( 96.49%, 92.66%, 88.08%, 68.39%,53.71% ), respectively.
References
K. Miyoshi and Y. Morimura, “Clinical manifestations of neuropsychiatric disorders,” in
Neuropsychiatric disorders, Springer, 2010, pp. 1–14.
K. H. Taber, R. A. Hurley, and S. C. Yudofsky, “Diagnosis and treatment of neuropsychiatric
disorders,” Annu. Rev. Med., vol. 61, pp. 121–133, 2010.
L. C. Fonseca, G. M. A. S. Tedrus, C. de Moraes, A. de V. Machado, M. P. de Almeida, and
D. O. F. de Oliveira, “Epileptiform abnormalities and quantitative EEG in children with attentiondeficit/
hyperactivity disorder,” Arq. Neuropsiquiatr., vol. 66, pp. 462–467, 2008.
R. C. Kessler et al., “The effects of temporally secondary co-morbid mental disorders on the
associations of DSM-IV ADHD with adverse outcomes in the US National Comorbidity Survey
Replication Adolescent Supplement (NCS-A),” Psychol. Med., vol. 44, no. 8, pp. 1779–1792,
R. B. Tenenbaum et al., “Response inhibition, response execution, and emotion regulation among
children with attention-deficit/hyperactivity disorder,” J. Abnorm. Child Psychol., vol. 47, no. 4,
pp. 589–603, 2019.
F. Lenzi, S. Cortese, J. Harris, and G. Masi, “Pharmacotherapy of emotional dysregulation in
adults with ADHD: a systematic review and meta-analysis,” Neurosci. Biobehav. Rev., vol. 84, pp.
–367, 2018.
A. Thapar, M. Cooper, R. Jefferies, and E. Stergiakouli, “What causes attention deficit
hyperactivity disorder?,” Arch. Dis. Child., vol. 97, no. 3, pp. 260–265, 2012, doi:
1136/archdischild-2011-300482.
L. Dubreuil-Vall, G. Ruffini, and J. A. Camprodon, “Deep Learning Convolutional Neural
Networks Discriminate Adult ADHD From Healthy Individuals on the Basis of Event-Related
Spectral EEG,” Front. Neurosci., vol. 14, no. April, pp. 1–12, 2020, doi:
3389/fnins.2020.00251.
M. C. Maya-Piedrahita, D. Cárdenas-Peña, and A. A. Orozco-Gutierrez, “Diagnosis of
attention deficit and hyperactivity disorder (ADHD) using hidden Markov models,” in 2020 28th
European Signal Processing Conference (EUSIPCO), 2021, pp. 1205–1209.
B. Taghibeyglou, N. Hasanzadeh, F. Bagheri, and M. Jahed, “ADHD diagnosis in children
using common spatial pattern and nonlinear analysis of filter banked EEG,” 2020 28th Iran. Conf.
Electr. Eng. ICEE 2020, pp. 0–4, 2020, doi: 10.1109/ICEE50131.2020.9260711.
A. Einizade, M. Mozafari, M. Rezaei-Dastjerdehei, E. Aghdaei, A. M. Mijani, and S.
Hajipour Sardouie, “Detecting ADHD children based on EEG signals using Graph Signal
Processing techniques,” 27th Natl. 5th Int. Iran. Conf. Biomed. Eng. ICBME 2020, no. November,
pp. 264–270, 2020, doi: 10.1109/ICBME51989.2020.9319456.
M. Moghaddari, M. Z. Lighvan, and S. Danishvar, “Diagnose ADHD disorder in children using
convolutional neural network based on continuous mental task EEG,” Comput. Methods Programs
Biomed., vol. 197, Dec. 2020, doi: 10.1016/j.cmpb.2020.105738.
R. Catherine Joy, S. Thomas George, A. Albert Rajan, and M. S. P. Subathra, “Detection of
adhd from eeg signals using different entropy measures and ann,” Clin. EEG Neurosci., vol. 53,
no. 1, pp. 12–23, 2022.
A. Ekhlasi, A. M. Nasrabadi, and M. Mohammadi, “Classification of the children with ADHD
and healthy children based on the directed phase transfer entropy of EEG signals,” Front. Biomed.
Technol., vol. 8, no. 2, pp. 115–122, 2021, doi: 10.18502/fbt.v8i2.6515.
A. M. Nasrabadi, A. Allahverdy, M. Samavati, and M. R. Mohammadi, “EEG data for
ADHD/Control children,” IEEE Dataport, 2020.
A. Allahverdy, A. K. Moghaddam, M. R. Mohammadi, and A. M. Nasrabadi, “Detecting
ADHD Children using the Attention Continuity as Nonlinear Feature of EEG,” 2016.
W. M. Lwin, T. Aung, and K. K. Wai, “Applications of Wavelet Transform,” 2019.
A. Teolis and J. J. Benedetto, Computational signal processing with wavelets, vol. 182. Springer,
S. Agarwal, Data mining: Data mining concepts and techniques. 2014. doi:
1109/ICMIRA.2013.45.
A. K. Jain, R. P. W. Duin, and J. Mao, “Statistical pattern recognition: A review,” IEEE Trans.
Pattern Anal. Mach. Intell., vol. 22, no. 1, pp. 4–37, 2000.
S. Natek and M. Zwilling, “Student data mining solution–knowledge management system related
to higher education institutions,” Expert Syst. Appl., vol. 41, no. 14, pp. 6400–6407, 2014.
R. Xu and D. Wunsch, “Survey of clustering algorithms,” IEEE Trans. neural networks, vol. 16,
no. 3, pp. 645–678, 2005.
J. A. K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, and J. Vandewalle, “Least
squares support vector machines, World Scientific Publishing, Singapore,” 2002.
J. Han, M. Kamber, and J. Pei, “Data Mining: Concepts and Techniques [Internet]. Waltham.”
Elsevier, 2011.
A. Burkov, The hundred-page machine learning book, vol. 1. Andriy Burkov Quebec City, QC,
Canada, 2019.
R. Kohavi, “Glossary of terms,” Spec. issue Appl. Mach. Learn. Knowl. Discov. Process, vol. 30,
no. 271, pp. 127–132, 1998.
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