Gokhan Guney, Esra Kisacik, Canan Kalaycioglu, Gorkem Saygili

Exploring the attention process differentiation of attention deficit hyperactivity disorder (ADHD) symptomatic adults using artificial intelligence on electroencephalography (EEG) signals

Attention deficit and hyperactivity disorder (ADHD) onset in childhood and its symptoms can last up till adulthood. Recently, electroencephalography (EEG) has emerged as a tool to investigate the neurophysiological connection of ADHD and the brain. In this study, we investigated the differentiation of attention process of healthy subjects with or without ADHD symptoms under visual continuous performance test (VCPT). In our experiments, artificial neural network (ANN) algorithm achieved 98.4% classification accuracy with 0.98 sensitivity when P2 event related potential (ERP) was used. Additionally, our experimental results showed that fronto-central channels were the most contributing. Overall, we conclude that the attention process of adults with or without ADHD symptoms become a key feature to separate individuals especially in fronto-central regions under VCPT condition. In addition, using P2 ERP component under VCPT task can be a highly accurate approach to investigate EEG signal differentiation on ADHD-symptomatic adults.

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[1] Wender P, Wolf Lo, Wasserstein J. Adults with ADHD: An Overview. Annals of the New York Academy of Sciences 2001; 931(1):1-16.
[2] Polanczyk G, De Lima MS, Horta BL, Biederman J, Rohde LA. The worldwide prevalence of ADHD: A systematic review and metaregression analysis. American Journal of Psychiatry 2007; 164: 942-948.
[3] Zang YF, Yong H, Chao-Zhe Z, Qing-Jiu C, Man-Qiu S et al. Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain and Development 2007; 29: 83-91.
[4] Zang YF, Jin Z, Weng XC, Zhang L, Zeng YW et al. Functional MRI in attention-deficit hyperactivity disorder: Evidence for hypofrontality. Brain and Development 2005; 27: 544-550.
[5] Rohde LA, Roman T, Szobot C, Cunha RD, Hutz MH et al. Dopamine transporter gene, response to methylphenidate and cerebral blood flow in attention-deficit/hyperactivity disorder: A pilot study. Synapse 2003; 48: 87-89.
[6] Szobot C, Roman T, Cunha R, Acton P, Hutz M et al. Brain perfusion and dopaminergic genes in boys with attention-deficit/ hyperactivity disorder. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 2005; 132 B: 53-58.
[7] Schneider M, Retz W, Coogan A, Thome J, Rösler M. Anatomical and functional brain imaging in adult attentiondeficit/ hyperactivity disorder (ADHD) - A neurological view. European archives of psychiatry and clinical neuroscience 2006; 256: i32-41.
[8] Castellanos FX, Lee PP, Sharp W, Jeffries NO,Greenstein DK et al. Developmental trajectories of brain volume abnormalities in children and adolescents with attention-deficit/hyperactivity disorder. Journal of the American Medical Association 2002; 288: 17401748.
[9] Loo SK, Specter E, Smolen A, Hopper C, Teale PD et al. Functional effects of the DAT1 polymorphism on EEG measures in ADHD. Journal of the American Academy of Child and Adolescent Psychiatry 2003; 42: 986-993.
[10] Gevensleben H, Holl B, Albrecht B, Schlamp D, Kratz O et al. Distinct EEG effects related to neurofeedback training in children with ADHD: A randomized controlled trial. International Journal of Psychophysiology 2009; 74: 149–57.
[11] Başar E, Başar-Eroglu C, Karakaş S, Schürmann M. Gamma, alpha, delta, and theta oscillations govern cognitive processes.International Journal of Psychophysiology 2001; 39: 241-248.
[12] Luck SJ, Hillyard SA. Electrophysiological correlates of feature analysis during visual search. Psychophysiology 1994; 31: 291-308.
[13] Başar-Eroglu C, Demiralp T. Event-related theta oscillations: An integrative and comparative approach in the human and animal brain. International Journal of Psychophysiology 2001; 39: 167-195.
[14] Bokura H, Yamaguchi S, Kobayashi S. Electrophysiological correlates for response inhibition in a Go/NoGo task.Clinical Neurophysiology 2001; 112: 2224-232.
[15] Doallo S, Lorenzo-López L, Vizoso C, Holguín SR, Amenedo E et al. Modulations of the visual N1 component of event-related potentials by central and peripheral cueing. Clinical Neurophysiology 2005; 116: 807-820.
[16] Prox V, Dietrich DE, Zhang Y, Emrich HM, Ohlmeier MD. Attentional processing in adults with ADHD as reflected by event-related potentials. Neuroscience Letters 2007; 419: 236-241.
[17] Karamacoska D, Barry RJ, Steiner GZ, de Blasio FM. Clarifying the sequential processes involved in a cued continuous performance test. Psychophysiology 2015; 52: 67-80.
[18] Kirmizi-Alsan E, Bayraktaroglu Z, Gurvit H, Keskin YH, Emre M et al. Comparative analysis of event-related potentials during Go/NoGo and CPT: Decomposition of electrophysiological markers of response inhibition and sustained attention. Brain Research 2006; 1104: 114-128.
[19] Jonkman LM. The development of preparation, conflict monitoring and inhibition from early childhood to young adulthood; a Go/Nogo ERP study. Brain Research 2006; 1097: 181-193.
[20] Oades RD, Dittmann-Balcar A, Schepker R, Eggers C, Zerbin D. Auditory event-related potentials (ERPs) and mismatch negativity (MMN) in healthy children and those with attention-deficit or tourette/tic symptoms. Biological Psychology 1996; 43: 163-185.
[21] Spronk M, Jonkman LM, Kemner C. Response inhibition and attention processing in 5- to 7-year-old children with and without symptoms of ADHD: An ERP study. Clinical Neurophysiology 2008; 119: 2738-2752.
[22] Bekker E, Kenemans J, Verbaten MN. Electrophysiological correlates of attention, inhibition, sensitivity and bias in a continuous performance task. Clinical Neurophysiology 2004; 115.9: 2001-2013.
[23] Picton TW. The P300 wave of the human event-related potential. Journal of Clinical Neurophysiology 1992; 9: 456-479.
[24] Mueller A, Candrian G, Kropotov JD, Ponomarev VA, Baschera GM. Classification of ADHD patients on the basis of independent ERP components using a machine learning system. Nonlinear Biomedical Physics 2010; 4: 1-12.
[25] Mueller A, Candrian G, Grane VA, Kropotov JD, Ponomarev VA et al. Discriminating between ADHD adults and controls using independent ERP components and a support vector machine: A validation study. Nonlinear Biomedical Physics 2011; 5.
[26] Tenev A, Markovska-Simoska S, Kocarev L, Pop-Jordanov J, Müller A et al. Machine learning approach for classification of ADHD adults. International Journal of Psychophysiology 2014; 93: 162-6.
[27] Biederman J, Hammerness P, Sadeh B, Peremen Z, Amit A et al. Diagnostic utility of brain activity flow patterns analysis in attention deficit hyperactivity disorder. Psychological Medicine 2017; 47: 1259-1270.
[28] Corkum PV, Siegel LS. Is the continuous performance task a valuable research tool for use with children with attention-deficit-hyperactivity-disorder? Journal of Child Psychology and Psychiatry 1993; 34: 1217-1239.
[29] Riccio CA, Reynolds CR, Lowe P, Moore JJ. The continuous performance test: A window on the neural substrates for attention? Archives of Clinical Neuropsychology 2002; 17: 235-272.
[30] Breiman L. Random forests. Machine Learning 2001; 45: 5-32.
[31] Turgay A. Adult Hyperactivity Assessment Scale based on DSM IV (unpublished scale). Integrative Therapy Institute Toronto, Canada, 1995.
[32] Kısacık E. Dikkat eksikliği ve hiperaktivite bozukluğu (DEHB) belirtileri gösteren ve göstermeyen sağlıklı erişkinlerde dikkat süreçlerinin EEG ile incelenmesi. pHD, Ankara Üniversitesi, Ankara, Türkiye, 2018 (in Turkish).
[33] Ercan E, Amado S, Somer O, Çıkoğlu S. Dikkat eksikliği hiperaktivite bozukluğu ve yıkıcı davranım bozuklukları için bir test bataryası geliştirme çabası. Çocuk ve Gençlik Ruh Sağlığı Derg 2001; 8: 132-144 (in Turkish).
[34] Luck S. Ten Simple Rules for Deisgning ERP Experiments. Event-related Potentials a Methods Handbook. London, England:MIT Press, 2005, pp. 17-32.
[35] Holcomb PJ. Automatic and attentional processing: An event-related brain potential analysis of semantic priming. Brain and Language 1988; 35: 66-85.
[36] Chen M, Challita U, Saad W, Yin C, Debbah M. Artificial neural networks-based machine learning for wireless networks: a tutorial. IEEE Communications Surveys and Tutorials 2019; 21: 3039-3071.
[37] Ghassemi F, Hassan M, Tehrani-Doost M, Abootalebi V. Using non-linear features of EEG for ADHD/normal participants’ classification. In: Procedia-Social and Behavioral Sciences-4th International Conference of Cognitive Science; Tehran,Iran, 2012; pp. 148-152.
[38] Satterfield JH, Schell AM, Nicholas T. Preferential neural processing of attended stimuli in attention-deficit hyperactivity disorder and normal boys. Psychophysiology 1994; 31: 1-10.
[39] Handy TC. Event-related potentials: A methods handbook. MIT press, 2005
[40] Kaur S, Singh S, Arun P, Kaur D, Bajaj M. Phase space reconstruction of EEG signals for classification of ADHD and control adults. Clinical EEG and Neuroscience. 2020; 51 (2): 102-113.