Classification of the Children with ADHD and Healthy Children Based on the Directed Phase Transfer Entropy of EEG Signals
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Abstract
Purpose: The present study was conducted to investigate and classify two groups of healthy children and children with Attention Deficit Hyperactivity Disorder (ADHD) by Effective Connectivity (EC) measure. Since early detection of ADHD can make the treatment process more effective, it is important to diagnose it using new methods.
Materials and Methods: For this purpose, Effective Connectivity Matrices (ECMs) were constructed based on Electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children of the same age. ECMs of each individual were obtained by the directed Phase Transfer Entropy (dPTE) between each pair of electrodes. ECMs were calculated in five frequency bands including, delta, theta, alpha, beta, and gamma. Based on ECM, an Effective Connectivity Vector (ECV) was constructed as a feature vector for the classification process. Furthermore, ECV of different frequency bands was pooled in one global ECV (gECV). Multilayer Artificial Neural Network (ANN) was used in the steps of classification and feature selection by the Genetic Algorithm (GA).
Results: The highest classification accuracy with the selected features of ECV was related to theta frequency band with 89.7%. After that, the delta frequency band had the highest accuracy with 89.2%. The results of ANN classification and GA on the gECV reported 89.1% of accuracy.
Conclusion: Our findings show that the dPTE measure, which determines effective connectivity between the brain regions, can be used to classify between ADHD and healthy groups. The results of the classification have improved compared to some studies that used the functional connectivity measures.
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29- A. Allahverdy, A. K. Moghadam, M. R. Mohammadi, and A. M. Nasrabadi, "Detecting ADHD Children using the Attention Continuity as Nonlinear Feature of EEG," Frontiers in Biomedical Technologies, vol. 3, no. 1-2, pp. 28-33, 2016.
30- M. Demuru et al., "Functional and effective whole brain connectivity using magnetoencephalography to identify monozygotic twin pairs," Scientific reports, vol. 7, no. 1, pp. 1-11, 2017.
31- T. T. Erguzel, S. Ozekes, O. Tan, and S. Gultekin, "Feature selection and classification of electroencephalographic signals: an artificial neural network and genetic algorithm based approach," Clinical EEG and neuroscience, vol. 46, no. 4, pp. 321-326, 2015.
32- P. Barttfeld et al., "Functional connectivity and temporal variability of brain connections in adults with attention deficit/hyperactivity disorder and bipolar disorder," Neuropsychobiology, vol. 69, no. 2, pp. 65-75, 2014.
2- R. 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)," Psychological medicine, vol. 44, no. 8, p. 1779, 2014.
3- R. B. Tenenbaum et al., "Response inhibition, response execution, and emotion regulation among children with attention-deficit/hyperactivity disorder," Journal of abnormal child psychology, vol. 47, no. 4, pp. 589-603, 2019.
4- F. Lenzi, S. Cortese, J. Harris, and G. Masi, "Pharmacotherapy of emotional dysregulation in adults with ADHD: a systematic review and meta-analysis," Neuroscience & Biobehavioral Reviews, vol. 84, pp. 359-367, 2018.
5- R. J. Barry, A. R. Clarke, and S. J. Johnstone, "A review of electrophysiology in attention-deficit/hyperactivity disorder: I. Qualitative and quantitative electroencephalography," Clinical neurophysiology, vol. 114, no. 2, pp. 171-183, 2003.
6- K. Sadatnezhad, R. Boostani, and A. Ghanizadeh, "Classification of BMD and ADHD patients using their EEG signals," Expert Systems with Applications, vol. 38, no. 3, pp. 1956-1963, 2011.
7- A. Allahverdy, A. M. Nasrabadi, and M. R. Mohammadi, "Detecting ADHD children using symbolic dynamic of nonlinear features of EEG," in 2011 19th Iranian Conference on Electrical Engineering IEEE, pp. 1-4, 2011.
8- M. R. Mohammadi, A. Khaleghi, A. M. Nasrabadi, S. Rafieivand, M. Begol, and H. Zarafshan, "EEG classification of ADHD and normal children using non-linear features and neural network," Biomedical Engineering Letters, vol. 6, no. 2, pp. 66-73, 2016.
9- A. D. Nazhvani, R. Boostani, S. Afrasiabi, and K. Sadatnezhad, "Classification of ADHD and BMD patients using visual evoked potential," Clinical neurology and neurosurgery, vol. 115, no. 11, pp. 2329-2335, 2013.
10- A. Mueller, G. Candrian, V. A. Grane, J. D. Kropotov, V. A. Ponomarev, and G.-M. Baschera, "Discriminating between ADHD adults and controls using independent ERP components and a support vector machine: a validation study," Nonlinear biomedical physics, vol. 5, no. 1, p. 5, 2011.
11- M. Ahmadlou and H. Adeli, "Wavelet-synchronization methodology: a new approach for EEG-based diagnosis of ADHD," Clinical EEG and Neuroscience, vol. 41, no. 1, pp. 1-10, 2010.
12- M. Ahmadlou and H. Adeli, "Functional community analysis of brain: a new approach for EEG-based investigation of the brain pathology," Neuroimage, vol. 58, no. 2, pp. 401-8, Sep 15 2011.
13- E. Pereda, M. García-Torres, B. Melián-Batista, S. Mañas, L. Méndez, and J. J. González, "The blessing of Dimensionality: Feature Selection outperforms functional connectivity-based feature transformation to classify ADHD subjects from EEG patterns of phase synchronisation," PloS one, vol. 13, no. 8, p. e0201660, 2018.
14- H. Kiiski et al., "Functional EEG connectivity is a neuromarker for adult attention deficit hyperactivity disorder symptoms," Clinical Neurophysiology, vol. 131, no. 1, pp. 330-342, 2020.
15- S. M. Snyder and J. R. Hall, "A meta-analysis of quantitative EEG power associated with attention-deficit hyperactivity disorder," Journal of Clinical Neurophysiology, vol. 23, no. 5, pp. 441-456, 2006.
16- S. K. Loo and S. Makeig, "Clinical utility of EEG in attention-deficit/hyperactivity disorder: a research update," Neurotherapeutics, vol. 9, no. 3, pp. 569-587, 2012.
17- M. Lobier, F. Siebenhuhner, S. Palva, and J. M. Palva, "Phase transfer entropy: a novel phase-based measure for directed connectivity in networks coupled by oscillatory interactions," Neuroimage, vol. 85 Pt 2, pp. 853-72, Jan 15 2014.
18- T. Schreiber, "Measuring information transfer," Phys Rev Lett, vol. 85, no. 2, pp. 461-4, Jul 10 2000.
19- M. Wibral, R. Vicente, and M. Lindner, "Transfer entropy in neuroscience," in Directed information measures in neuroscience: Springer, 2014, pp. 3-36.
20- L. I. Boon, A. Hillebrand, K. T. O. Dubbelink, C. J. Stam, and H. W. Berendse, "Changes in resting-state directed connectivity in cortico-subcortical networks correlate with cognitive function in Parkinson’s disease," Clinical Neurophysiology, vol. 128, no. 7, pp. 1319-1326, 2017.
21- M. M. A. Engels et al., "Directional information flow in patients with Alzheimer's disease. A source-space resting-state MEG study," Neuroimage Clin, vol. 15, pp. 673-681, 2017.
22- A. Hillebrand et al., "Direction of information flow in large-scale resting-state networks is frequency-dependent," Proceedings of the National Academy of Sciences, vol. 113, no. 14, pp. 3867-3872, 2016.
23- M. Dauwan et al., "EEG-directed connectivity from posterior brain regions is decreased in dementia with Lewy bodies: a comparison with Alzheimer's disease and controls," Neurobiology of aging, vol. 41, pp. 122-129, 2016.
24- A. Ahmadi, S. Davoudi, M. Behroozi, and M. R. Daliri, "Decoding covert visual attention based on phase transfer entropy," Physiology & behavior, vol. 222, p. 112932, 2020.
25- F. Hasanzadeh, M. Mohebbi, and R. Rostami, "Graph theory analysis of directed functional brain networks in major depressive disorder based on EEG signal," J Neural Eng, vol. 17, no. 2, p. 026010, Mar 27 2020.
26- J. Wang, J. Wang, J. Sun, S. Tong, and X. Hong, "Phase Transfer Entropy between Frontal and Posterior Regions during Visual Spatial Attention," in 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), IEEE, pp. 550-553, 2019.
27- S. Wang et al., "A Study on Resting EEG Effective Connectivity Difference before and after Neurofeedback for Children with ADHD," Neuroscience, 2021.
28- A. A. Ali Moti Nasrabadi, Mehdi Samavati, Mohammad Reza Mohammadi. (2020). EEG data for ADHD / Control children. Available: http://dx.doi.org/10.21227/rzfh-zn36
29- A. Allahverdy, A. K. Moghadam, M. R. Mohammadi, and A. M. Nasrabadi, "Detecting ADHD Children using the Attention Continuity as Nonlinear Feature of EEG," Frontiers in Biomedical Technologies, vol. 3, no. 1-2, pp. 28-33, 2016.
30- M. Demuru et al., "Functional and effective whole brain connectivity using magnetoencephalography to identify monozygotic twin pairs," Scientific reports, vol. 7, no. 1, pp. 1-11, 2017.
31- T. T. Erguzel, S. Ozekes, O. Tan, and S. Gultekin, "Feature selection and classification of electroencephalographic signals: an artificial neural network and genetic algorithm based approach," Clinical EEG and neuroscience, vol. 46, no. 4, pp. 321-326, 2015.
32- P. Barttfeld et al., "Functional connectivity and temporal variability of brain connections in adults with attention deficit/hyperactivity disorder and bipolar disorder," Neuropsychobiology, vol. 69, no. 2, pp. 65-75, 2014.
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| Issue | Vol 8 No 2 (2021) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v8i2.6515 | |
| Keywords | ||
| Attention Deficit Hyperactivity Disorder Electroencephalography Phase Transfer Entropy Effective Connectivity Classification | ||
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How to Cite
1.
Ekhlasi A, Motie Nasrabadi A, Mohammadi M. Classification of the Children with ADHD and Healthy Children Based on the Directed Phase Transfer Entropy of EEG Signals. Frontiers Biomed Technol. 2021;8(2):115-122.
