Identification of Attention Deficit Hyperactivity Disorder Patients Using Wavelet-Based Features of EEG Signals

Subject Areas : Renewable energy

Sahar Karimi Shahraki ¹ (Department of Electrical Engineering- Najafabad Branch, Islamic Azad University, Najafabad, Iran.)
Mahdi Khezri ² (Digital Processing and Machine Vision Research Center- Najafabad Branch, Islamic Azad University, Najafabad, Iran)

Received: 2020-08-23 Accepted : 2020-12-18 Published : 2021-11-22

Keywords: ADHD, Wavelet Transform, Time Domain Features, kNN classifier, SVM Classifier,

Abstract :

Attention Deficit Hyperactivity Disorder (ADHD) is a neurological and psychiatric disorder which causes to attention deficit, anxiety, hyperactivity and impulsive behaviors. ADHD is more common in children and directly leads to their learning disability. The aim of this study was to accurately identify ADHD patients by using wavelet-based features of brain signals (EEG). Recorded EEG signals from 61 children with ADHD (diagnosed according to the DSM-IV criteria) and 60 healthy controls in the age range of 7-12 years were used to design the system. In the proposed method by applying wavelet transform, EEG signals were decomposed into subbands; for the time version of the signals in each subband, the temporal and statistical features were calculated. The reduced feature set by principal component analysis (PCA) method was then used to train the classification unit to identify ADHD patients from healthy individuals. To obtain the desired results, different types of wavelet functions and decomposition levels were investigated. The bior3.1 wavelet function with the support vector machine (SVM) classifier and the rbio1.1 wavelet function with the k-nearest neighbor (kNN) classifier presented the best performance with the recognition accuracy of 98.33% and 99.17%, respectively. The SVM classification method with radial basis kernel function (RBF) and the kNN method with the number of nearest neighbors, k = 3 obtained the best results.The results obtained in this study compared to the results reported in previous studies showed at least a 2% improvement in the recognition accuracy of ADHD patients.

References:

[1] L. D. Adler, A. Nierenberg, "Review of medication adherence in children and adults with ADHD", Postgraduate Medicine, vol. 122, no. 1, pp. 184-191, Jan. 2010 (doi:10.3810/pgm.2010.01.2112).

[2] S. C. Yeh, S. Y. Lin, E. H. K. Wu, K. F. Zhang, X. Xiu, A. Rizzo, C. R. Chung, "A virtual-reality system integrated with neuro-behavior sensing for attention-deficit/hyperactivity disorder intelligent assessment", IEEE Trans. on Neural Systems and Rehabilitation Engineering, vol. 28, no. 9, pp. 1899-1907, Sept. 2020 (doi: 10.1109/TNSRE.2020.3004545).

[3] S. Kim, J. Ryu, Y. Choi, Y. Kang, H. Li and K. Kim, "Eye-contact game using mixed reality for the treatment of children with attention deficit hyperactivity disorder", IEEE Access, vol. 8, pp. 45996-46006, 2020 (doi: 10.1109/ACCESS.2020.2977688).

[4] J. T. Nigg, L. G. Blakey, C. L. Huang-Pollock, M.D. Rappley, "Neuropsychological executive functions and DSM-IV ADHD subtypes", Journal of the American Academy of Child and Adolescent Psychiatry, vo. 41, no. 1, pp. 59- 66, Jan. 2002 (doi:10.1097/00004583-200201000-00012).

[5] N. Lofthouse, L. E. Arnold, S. Hersch, E. Hurt, R. DeBeus, "A review of neurofeedback treatment for pediatric ADHD", Journal of Attention Disorders, vol. 16, no. 5, pp. 351-372, Jul. 2012 (doi: 10.117­7/10­870­54711427530).

[6] S. Enriquez-Geppert, D. Smit, M.G. Pimenta, M. Arns, "Neurofeedback as a treatment intervention in ADHD: Current evidence and practice", Current Psychiatry Reports, vol. 21, no. 6, pp. 46, May. 2019 (doi: 10.­10­07/s11920-019-1021-4).

[7] I. Ebrahimnejad, M. Kahkesh, A. Naghsh, "Classification and feature extraction of electroencephalogram signals for epilepsy using PCA, ICA, DWT and SVM methods", Journal of Intelligent Procedures in Electrical Technology, vol. 9, no. 36, pp. 15-22, Winter 2019 (in Persian).

[8] N. Behzadfar, S. M. P. Firoozabadi, K. Badie, "Analysis of regularity in the EEG before/after working memory task", Proceeding of the IEEE/ICBME, pp. 1-5, Tehran, Iran, Dec. 2017 (doi: 10.1109/ICBME.20­1­7.8­430260).

[9] H. Akbari, S. Saraf-Esmaili, S. Farzollah-Zadeh, "Detection of seizure EEG signals based on reconstructed phase space of rhythms in EWT domain and genetic algorithm", Signal Processing and Renewable Energy, vol. 4, no. 2, pp. 23-36, Spring 2020.

[10] M. Khezri, M. Jahed, "Introducing a new multi-wavelet function suitable for sEMG signal to identify hand motion commands", Proceeding of the IEEE/IEMBS, pp. 1924-1927, Lyon, France, Aug. 2007 doi: 10.1109/IEMBS.2007.4352693.

[11] M. Dorvashi, N. Behzadfar, G. Shahgholian, "Classification of alcoholic and non-alcoholic individuals based on frequency and non-frequency features of electroencephalogram signal", Iranian Journal of Biomedical Engineering, vol. 14, no. 2, pp. 121-130, Summer 2020 (doi: 10.22041/ijb­me.202­0.119­841.1­5­5­1­) (in Persian).

[12] N. Dashti, M. Khezri, "Recognition of motor imagery based on dynamic features of EEG signals", Journal of Intelligent Procedures in Electrical Technology, vol. 11, no. 43, pp. 13-27, Autumn 2020 (in Persian).

[13] M. Congedo, J. F. Lubar, D. Joffe, "Low-resolution electromagnetic tomography neurofeedback", IEEE Trans. on Neural Systems and Rehabilitation Engineering, vol. 12, no. 4, pp. 387-397, Dec. 2004 (doi: 10.1­10­9/TNSRE.2004.840492).

[14] M. Adamou, T. Fullen, S. L. Jones, "EEG for diagnosis of adult ADHD: a systematic review with narrative analysis", Frontiers in Psychiatry, vol. 11, pp. 871, Aug. 2020 (doi: 10.3389/fpsyt.2020.00871).

[15] M. Cerquera, M. Arns, R.M. Gutiérrez, J. Freund, "Dynamical measures for characterization of EEG registers in patients with attention deficit hyperactivity disorder treated with neurofeedback", Proceeding of the IEEE/STSIVA, pp. 213-217, Antioquia, Colombia, Sep. 2012 (doi:10.1109/STSIVA.2012.6340584).

[16] Q. Wang and O. Sourina, "Real-time mental arithmetic task recognition from EEG signals", IEEE Trans. on Neural Systems and Rehabilitation Engineering, vol. 21, no. 2, pp. 225-232, Jan. 2013 (doi: 10.1109/T­N­SR­E.2­­012.2236576).

[17] A. Vahid, A. Bluschke, V. Roessner, S. Stober, C. Beste, "Deep learning based on event-related EEG differentiates children with ADHD from healthy controls", Journal of Clinical Medicine, vol. 8, no.7, pp. 1055, July 2019 (doi:10.3390/jcm8071055).

[18] M. R. Mohammadi, A. Khaleghi, A. M. Nasrabadi, S. Rafieivand, M. Begol, H. Zarafshan, "EEG classific­ation of ADHD and normal children using non-linear features and neural network", Biomedical Engineering Letters, vol. 6, pp. 66–73, June 2016 (doi:10.1007/s13534-016-0218-2).

[19] A. E. Alchalabi, S. Shirmohammadi, A. N. Eddin, M. Elsharnouby, "FOCUS: detecting ADHD patients by an EEG-based serious game", IEEE Trans. on Instrumentation and Measurement, vol. 67, no. 7, pp. 1512-1520, June 2018 (doi: 10.1109/TIM.2018.2838158).

[20] Y. K. Boroujeni, A. A. Rastegari, H. Khodadadi, "Diagnosis of attention deficit hyperactivity disorder using non-linear analysis of the EEG signal", IET Systems Biology, vol. 13, no. 5, pp. 260-266, Oct. 2019 (doi: 10.10­49/iet-syb.2018.5130).

[21] H. Chen, Y. Song, X. Li, "A deep learning framework for identifying children with ADHD using an EEG-based brain network", Neurocomputing, vol. 356, pp. 83-96, Sept. 2019 (doi: 10.1016/j.ne­ucom.­201­9.0­4.0­5­8­).

[22] J. Kevric, A. Subasi, "Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system", Biomedical Signal Processing and Control, vol. 31, pp.398-406, Jan. 2017 (doi: 10.1016/j.bspc.2016.09.007).

[23] M. Zecca, S. Micera, M. C. Carrozza, P. Dario, "Control of multifunctional prosthetic hands by processing the electromyographic signal", Critical Reviews in Biomedical Engineering, vol. 30, no. 4-6, pp. 459–485, 2002 (doi: 10.1615/critrevbiomedeng. v30.i456.80).

[24] P. C. Petrantonakis, L. J. Hadjileontiadis, "Emotion recognition from EEG using higher order crossings", IEEE Trans. on Information Technology in Biomedicine, vol. 14, no. 2, pp. 186-197, Mar. 2010 (doi: 10.11­09/T­ITB.2009.2034649).

[25] C. A. Frantzidis, C. Bratsas, C. L. Papadelis, E. Konstantinidis, C. Pappas, P. D. Bamidis, "Toward emotion aware computing: An integrated approach using multichannel neurophysiological recordings and affective visual stimuli", IEEE Trans. on Information Technology in Biomedicine, vol. 14, no. 3, pp. 589-597, May.  2010 (doi: 10.1109/TITB.2010.2041553).