Tytuł artykułu
Autorzy
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Attention-deficit/hyperactivity disorder (ADHD) is an important challenge in studies of children's ethology that unbalances the opposite behaviors for creating inattention along with or without hyperactivity. Nevertheless, most studies on the ADHD children, which employed the EEG signals for analyzing the ADHD influence on the brain activities, consid- ered the EEG signals as a random or chaotic process without considering the role of these opposites in the brain activities. In this study, we considered the EEG signals as a biotic process according to these opposites and examined the ADHD effect on the brain activity by defining the dual sets of transitions between states in the complement plots of quantized EEG segments. The results of this study generally indicated that the complement plots of quantized EEG signal have a surprising regularity similar to the Mandala patterns compared to the chaotic processes. These results also indicated that the probability of occurrence of dual sets in the complement plots of ADHD children was averagely different ( p < 0.01) from that of healthy children, so that the SVM classifier developed by these probabilities could significantly separate the ADHD from healthy children (99.37% and 98.25% for training and testing sets, respectively). Therefore, the complement plots of quantized EEG signals rele-vant to the ADHD children not only can quantify informational opposition caused by inattention, hyperactivity and impulsivity, but also these plots can provide remarkable information for developing new diagnostic and therapeutic techniques.
Wydawca
Czasopismo
Rocznik
Tom
Strony
425--437
Opis fizyczny
Bibliogr. 66 poz., rys., tab., wykr.
Twórcy
autor
- Faculty of New Sciences & Technologies, Semnan University, Semnan, Iran
autor
- Department of Electrical Engineering, Semnan University, Semnan, Iran
autor
- Department of Electrical Engineering, Semnan University, Semnan, Iran
Bibliografia
- [1] American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition: DSM-IV-TR®. American Psychiatric Association; 2000.
- [2] Yaghoobi Karimu R, Azadi S. Diagnosing the ADHD using a mixture of expert fuzzy models. Int J Fuzzy Syst 2017;1–15.
- [3] Clauss-Ehlers CS. Encyclopedia of cross-cultural school psychology. Springer US; 2010.
- [4] Sroubek A, Kelly M, Li X. Inattentiveness in attention-deficit/ hyperactivity disorder. Neurosci Bull 2013;29(1):103–10.
- [5] Pattanshetti N, Patil N, Tekkalaki B. Prevalence of adult attention deficit hyperactivity disorder in patients with bipolar affective disorder: a 1-year hospital-based cross-sectional study. Indian J Health Sci Biomed Res (KLEU) 2016;9(3):288–96.
- [6] Weiss M, Murray C. Assessment and management of attention-deficit hyperactivity disorder in adults. Can Med Assoc J 2003;168(6):715–22.
- [7] Silvana M-S, Nada P-J. Quantitative EEG in children and adults with attention deficit hyperactivity disorder. Clin EEG Neurosci 2016;48(1):20–32.
- [8] Poil SS, Bollmann S, Ghisleni C, OGorman RL, Klaver P, Ball J, et al. Age dependent electroencephalographic changes in attention-deficit/hyperactivity disorder (ADHD). Clin Neurophysiol 2014;125(8):1626–38.
- [9] Nazari MA, Wallois F, Aarabi A, Berquin P. Dynamic changes in quantitative electroencephalogram during continuous performance test in children with attention- deficit/hyperactivity disorder. Int J Psychophysiol 2011;81 (3):230–6.
- [10] Gani C. Long term effects after feedback of slow cortical potentials and of Theta/Beta – amplitudes in children with Attention Deficit Hyperactivity Disorder (ADHD). Int J Bioelectromagn 2008;10(4):209–32.
- [11] Doehnert M, Brandeis D, Straub M, Steinhausen HC, Drechsler R. Slow cortical potential neurofeedback in attention deficit hyperactivity disorder: is there neurophysiological evidence for specific effects? J Neural Transm 2008;115(10):1445–56.
- [12] Clarke AR, Robert B, Rory MC, Mark S. Electroencephalogram differences in two subtypes of attention-deficit/hyperactivity disorder. Psychophysiology 2001;38(2):212–21.
- [13] Barry RJ, Clarke AR, Johnstone SJ. A review of electrophysiology in attention-deficit/hyperactivity disorder: I. Qualitative and quantitative electroencephalography. Clin Neurophysiol 2003;114 (2):171–83.
- [14] Arns M, Heinrich H, Strehl U. Evaluation of neurofeedback in ADHD: the long and winding road. Biol Psychol 2014;95:108–15.
- [15] Pop-Jordanova N, Markovska-Simoska S, Zorcec T. Neurofeedback treatment of children with attention deficit hyperactivity disorder. Prilozi 2005;26(1):71–80.
- [16] Holtmann M, Sonuga-Barke E, Cortese S, Brandeis D. Neurofeedback for ADHD: a review of current evidence. Child Adolesc Psychiatr Clin N Am 2014;23(4):789–806.
- [17] Janssen TWP, Bink M, Weeda WD, Gelade K, Mourik RV, Maras A, et al. Learning curves of theta/beta neurofeedback in children with ADHD. Eur Child Adolesc Psychiatry 2017;26(5):573–82.
- [18] Leins U, Goth G, Hinterberger T, Klinger C, Rumpf N, Strehl U. Neurofeedback for children with ADHD: a comparison of SCP and theta/beta protocols. Appl Psychophysiol Biofeedback 2007;32(2):73–88.
- [19] Levesque J, Beauregard M, Mensour B. Effect of neurofeedback training on the neural substrates of selective attention in children with attention-deficit/ hyperactivity disorder: a functional magnetic resonance imaging study. Neurosci Lett 2006;394(3):216–21.
- [20] Sunohara GA, Malone MA, Rovet J, Humphries T, Roberts W, Taylor MJ. Effect of methylphenidate on attention in children with attention deficit hyperactivity disorder (ADHD): ERP evidence. Neuropsychopharmacology 1999;21 (2):218–28.
- [21] Fan J, McCandliss BD, Fossella J, Flombaum JI, Posne MI. The activation of attentional networks. Neuroimage 2005;26 (2):471–9.
- [22] Raz A. Anatomy of attentional networks. Anatom Rec B: New Anat 2004;281B(1):21–36.
- [23] Yaghoobi R, Azadi S. Lossless EEG compression using the DCT and the Huffman coding. J Sci Ind Res (JSIR) 2016;75 (10):615–20.
- [24] Sabelli HC. Bios: a study of creation. World Scientific; 2005.
- [25] Sabelli H, Novelty A. Measure of creative organization in natural and mathematical time series. Nonlinear Dyn Psychol Life Sci 2001;5(2):89–113.
- [26] Sabelli H. Arrangement, a measure of nonrandom complexity. Syst Anal Model Simul 2002;42(3):395–403.
- [27] Sabelli H. Complement plots: analyzing opposites reveals Mandala-like patterns in human heart beats. Int J Gener Syst 2000;29(5):799–830.
- [28] Sabelli H, Abouzeid A. Definition and empirical characterization of creative processes. Nonlinear Dyn Psychol Life Sci 2003;7(1):35–47.
- [29] Sabelli H, Carlson-Sabelli L. Bios, a process approach to living system theory. In honour of James and Jessie Miller. Syst Res Behav Sci 2006;23(3):323–36.
- [30] Sabelli H, Messer J, Kovacevic L, Walthall K. The biotic pattern of heartbeat intervals. Int J Cardiol 2010;145(2):303–4.
- [31] Sabelli H, Sugerman A, Kovacevic L, Kauffman L, Carlson- Sabelli L, Patel M, et al. Bios data analyzer. Nonlinear Dyn Psychol Life Sci 2005;9(4):505–38.
- [32] Kauffman L, Sabelli H. Mathematical bios. Kybernetes 2002;31:1418–28.
- [33] Sadeghi G, Sheikhani A, Ashrafzadeh F. Poincare section analysis of the electroencephalogram in autism spectrum disorder using complement plots. Kybernetes 2017;46 (2):364–82.
- [34] Goyette CH, Conners CK, Ulrich RF. Normative data on revised Conners parent and teacher rating scales. J Abnorm Child Psychol 1978;6(2):221–36.
- [35] Subha DP, Joseph PK, Acharya R, Lim CM. EEG signal analysis: a survey. J Med Syst 2010;34(2):195–212.
- [36] Pal PR, Khobragade P, Panda R. Expert system design for classification of brain waves and epileptic-seizure detection. Students' Technology Symposium (TechSym), IEEE; 2011.
- [37] Rodrıguez-Bermudez G, Garcıa-Laencina PJ. Analysis of EEG signals using nonlinear dynamics and chaos: a review. Appl Math Inf Sci 2015;9(5):2309–21.
- [38] Chappell D, Panagiotidis T. Using the correlation dimension to detect non-linear dynamics: evidence from the Athens Stock Exchange. EconWPA 2005.
- [39] Roschke J, Fell J, Beckmann P. The calculation of the first positive Lyapunov exponent in sleep EEG data. Electroencephalogr Clin Neurophysiol 1993;86(5):348–52.
- [40] Pijn JP, Neerven JV, Noestc A, Silvad FHL. Chaos or noise in EEG signals; dependence on state and brain site. Electroencephalogr Clin Neurophysiol 1991;79(5):371–81.
- [41] Hilborn RC. Chaos and nonlinear dynamics: an introduction for scientists and engineers. Oxford University Press; 2000.
- [42] Wang Q, Sourina O, Nguyen MK. Fractal dimension based neurofeedback in serious games. Vis Comput 2011;27 (4):299–309.
- [43] Yaghoobi R, Yaghoobi A. The effects of beta-I and fractal dimension neurofeedback on reaction time. Int J Intell Syst Technol Appl 2014;6(11):42–8.
- [44] Yaghoobi Karimui R, Azadi S. Cardiac arrhythmia classification using the phase space sorted by Poincare sections. Biocybern Biomed Eng 2017;37(4):690–700.
- [45] Lenartowicz A, Loo SK. Use of EEG to diagnose ADHD. Curr Psychiatry Rep 2014;16(11):1–11.
- [46] Williams LM, Hermens DF, Thein T, Clark CR, Cooper NJ, Clarke SD, et al. Using brain-based cognitive measures to support clinical decisions in ADHD. Pediatr Neurol 2010;42 (2):118–26.
- [47] Loo SK, Cho A, Hale TS, McGough J, McCracken J, Smalley SL. Characterization of the theta to beta ratio in ADHD: identifying potential sources of heterogeneity. J Atten Disord 2013;17(5):384–92.
- [48] Liechti MD, Valko L, Müller UC, Döhnert M, Drechsler R, Steinhausen HC, et al. Diagnostic value of resting electroencephalogram in attention-deficit/hyperactivity disorder across the lifespan. Brain Topogr 2013;26(1):135–51.
- [49] Buyck I, Wiersema JR. Resting electroencephalogram in attention deficit hyperactivity disorder: developmental course and diagnostic value. Psychiatry Res 2014;216(3):391–7.
- [50] Helgadóttir H, Gudmundsson ÓÓ, Baldursson G, Magnússon P, Blin N, Brynjólfsdóttir B, et al. Electroencephalography as a clinical tool for diagnosing and monitoring attention deficit hyperactivity disorder: a cross-sectional study. BMJ Open 2015;5(1):1–9.
- [51] Tenev A, Markovska-Simoska S, Kocarev L, Pop-Jordanov J, Müller A, Candrian G. Machine learning approach for classification of ADHD adults. Int J Psychophysiol 2014;93 (1):162–6.
- [52] Ogrim G, Kropotov J, Hestad K. The quantitative EEG theta/ beta ratio in attention deficit/hyperactivity disorder and normal controls: sensitivity, specificity, and behavioral correlates. Psychiatry Res 2012;198(3):482–8.
- [53] Sadatnezhad K, Boostani R, Ghanizadeh A. Classification of BMD and ADHD patients using their EEG signals. Expert Syst Appl 2011;38(3):1956–63.
- [54] Magee CA, Clarke AR, Barry RJ, McCarthy R, Selikowitz M. Examining the diagnostic utility of EEG power measures in children with attention deficit/hyperactivity disorder. Clin Neurophysiol 2005;116(5):1033–40.
- [55] Ahmadlou M, Adeli H. Wavelet-synchronization methodology: a new approach for EEG-based diagnosis of ADHD. Clin EEG Neurosci 2010;41:1–10.
- [56] Simoska S, Jordanova N. Quantitative EEG in children and adults with attention deficit hyperactivity disorder: comparison of absolute and relative power spectra and theta/beta ratio. Clin EEG Neurosci 2016;48(1):20–32.
- [57] Snyder SM, Quintana H, Sexson SB, Knott P, Haque AF, Reynolds DA. Multicenter validation of EEG and rating scales in identifying ADHD within a clinical sample. Psychiatry Res 2008;159(3):346–58.
- [58] Monastra VJ, Lubar JF, Michael L. The development of a quantitative electroencephalographic scanning process for attention deficit-hyperactivity disorder: reliability and validity studies. Neuropsychology 2001;15(1):136–44.
- [59] Mohammadi MR, Khaleghi A, Nasrabadi AM, Rafieivand S, Begol M, Zarafshan H. EEG classification of ADHD and normal children using non-linear features and neural network. Biomed Eng Lett 2016;6(2):66–73.
- [60] Abibullaev B, An J. Decision support algorithm for diagnosis of ADHD using electroencephalograms. J Med Syst 2012;36 (4):2675–88.
- [61] Berger H. Uber das elektroenkephalogram des Menschen: Zweite Mittelung. J Psychol Neurol (Lpz) 1930;40:160–79.
- [62] Wrobel A. Beta activity: a carrier for visual attention. Acta Neurobiol Exp (Warsz) 2000;60(2):247–60.
- [63] Mundy-Castle AC. Theta and beta rhythm in the electroencephalograms of normal adults. Electroencephalogr Clin Neurophysiol 1951;3(4):477–86.
- [64] Clarke AR, Barry RJ, McCarthy R, Selikowitz M. EEG analysis in Attention-Deficit/Hyperactivity Disorder: a comparative study of two subtypes. Psychiatry Res 1998;81(1):19–29.
- [65] Lansbergen MM, Arns M, Dongen-Boomsma MV, Spronk D, Buitelaar JK. The increase in theta/beta ratio on resting-state EEG in boys with attention-deficit/hyperactivity disorder is mediated by slow alpha peak frequency. Prog Neuropsychopharmacol Biol Psychiatry 2011;35(1):47–52.
- [66] Koehler S, Lauer P, Schreppel T, Jacob C, Heine M, Boreatti- Hümmer A, et al. Increased EEG power density in alpha and theta bands in adult ADHD patients. J Neural Transm 2009;116(1):97–104.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8dd1f3d8-f03b-4b19-835a-15412e06e5c2