نشریه علمی مهندسی پزشکی زیستی
Iranian Journal of Biomedical Engineering (IJBME)

تفکیک افراد سالم و ADHD در دوران کودکی و بزرگ‌سالی با استفاده از فضای فاز EEG مرتب شده توسط قطعات پوانکاره‌ی شعاعی

نوع مقاله : مقاله کامل پژوهشی

نویسندگان

بهناز شیخ الاسلامی 1
قاسم صادقی بجستانی 2
رضا یعقوبی کریموی 3
ریحانه ظریفیان 1

1 کارشناسی ارشد، گروه مهندسی پزشکی، دانشکده‌ی مهندسی، دانشگاه بین‌المللی امام رضا (ع)، مشهد، ایران

2 استادیار، گروه مهندسی پزشکی، دانشکده‌ی مهندسی، دانشگاه بین‌المللی امام رضا (ع)، مشهد، ایران

3 دکتری، گروه مهندسی پزشکی، دانشکده‌ی مهندسی، دانشگاه بین‌المللی امام رضا (ع)، مشهد، ایران

https://doi.org/10.22041/ijbme.2021.138631.1634
چکیده
اختلال کم‌توجهی/بیش‌فعالی (ADHD) یک اختلال رشدی عصبی است که می‌تواند در افراد با سنین مختلف به خصوص در کودکان ایجاد شده و سبب تغییر در رفتار آن‌ها شود. مطالعات گذشته اغلب روی پردازش‌های حوزه‌ی فرکانسی و یا جنبه‌های دینامیک غیرخطی سیگنال‌های EEG از قبیل بعد همبستگی، بعد فرکتال، نمای لیاپانوف، آنتروپی و نرخ بازگشت فرایندهای مغزی برای تفکیک افراد مبتلا به ADHD متمرکز بوده است. در این مطالعه با استفاده از قطاع‌های شعاعی پوانکاره در فضای فاز سیگنال‌های EEG افراد مبتلا به ADHD و افراد سالم در دو گروه خردسالان و بزرگ‌سالان، مرتب‌سازی این فضا و هم‌چنین استخراج ویژگی‌های هندسی مختلف، دیدگاه متفاوتی از میزان پیچیدگی فعالیت‌های مغزی و سطح پویایی افراد مبتلا به ADHD در مقایسه با افراد سالم ارائه شده و به ارزیابی حجم بستر نوسان سیگنال EEG پرداخته شده است. در نهایت با ارزیابی ویژگی‌های استخراج شده و استفاده از الگوریتم SFS بر مبنای طبقه‌بندی کننده‌ی RBF-SVM، تفکیک افراد مبتلا به ADHD از افراد سالم در دو گروه خردسالان و بزرگ‌سالان به ترتیب با صحت 04/2±20/93 و 13/1±60/95 انجام شده است. نتایج این تحقیق نشان داده که حجم بستر نوسان سیگنال EEG افراد مبتلا به ADHD نسبت به افراد سالم به طور قابل توجهی بیش‌تر بوده و این موضوع بیان‌گر افزایش میزان پویایی و در نتیجه کاهش میزان پیچیدگی فعالیت‌های مغزی در این افراد است. هم‌چنین در این پژوهش مشخص شده که افزایش حجم بستر نوسان سیگنال‌های EEG در کودکان نسبت به بزرگ‌سالان بیش‌تر بوده و این موضوع نشان دهنده‌ی افزایش سطح پویایی کودکان نسبت به بزرگ‌سالان است. بنابراین می‌توان ADHD و سن را به عنوان دو عامل مهم در افزایش حجم بستر نوسان سیگنال EEG معرفی کرد.

کلیدواژه‌ها

الکتروانسفالوگرافی
قطع پوانکاره
پیچیدگی
پویایی
بستر نوسان
کم‌توجهی-بیش‌فعالی

موضوعات

عنوان مقاله English

Separating the Healthy and ADHD People in Childhood and Adulthood using the EEG Phase Space Sorted by the Radial Poincare Sections

نویسندگان English

Behnaz Sheikholeslami 1
Ghasem Sadeghi Bajestani 2
Reza Yaghoobi Karimui 3
Reyhaneh Zarifiyan 1
1 M.Sc., Department of Medical Engineering, Faculty of Engineering, Imam Reza International University, Mashhad, Iran
2 Assistant Professor, Department of Medical Engineering, Faculty of Engineering, Imam Reza International University, Mashhad, Iran
3 Ph.D ., Department of Medical Engineering, Faculty of Engineering, Imam Reza International University, Mashhad, Iran
چکیده English

Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that can affect people of all ages in the community, especially children, and cause changes in their behavior. Previous studies have often focused on frequency domain processing or the nonlinear dynamic aspects of EEG signals such as correlation dimension, fractal dimension, Lyapunov exponent, entropy, and recurrence rate of brain processes to differentiate individuals with ADHD. In this study, we evaluate the volume of the EEG signal oscillation basin using Poincare sections in the phase space of EEG signals of people with ADHD and healthy people and sort this space as well as extract various geometric features. We present a different perspective of complexity of brain activity and the level of dynamism of people with ADHD compared to healthy individuals. Finally, by evaluating the extracted features and using the SFS algorithm based on the RBF-SVM classifier, we were able to separate people with ADHD from healthy people in the groups of children and adults, with accuracy of 93.20±2.04 and 95.60±1.13. The results of this study showed that the volume of the EEG signal oscillation basin in people with ADHD was significantly higher than healthy people, which indicates an increase in the degree of dynamism and thus a decrease in the complexity of brain activity in these people. It was also identified in this study that the increase in the volume of the EEG signal oscillation basin in children is more than adults, which indicates an increase in the level of dynamism of children compared to adults. Therefore, ADHD and age can be introduced as two important factors in changing the volume of the EEG signal oscillation basin.

کلیدواژه‌ها English

Electroencephalogram
Poincare Section
Complexity
Dynamism
Oscillation Basin
ADHD
  1. Kaur, S. Singh, P. Arun, D. Kaur, and M. Bajaj, "Phase Space Reconstruction of EEG Signals for Classification of ADHD and Control Adults," Clinical EEG and Neuroscience, vol. 51, no. 2, pp. 102-113, 2020.
  2. -N. Yang, Y.-M. Tai, L.-K. Yang, and S. S.-F. Gau, "Prediction of childhood ADHD symptoms to quality of life in young adults: adult ADHD and anxiety/depression as mediators," Research in developmental disabilities, vol. 34, no. 10, pp. 3168-3181, 2013.
  3. Sridhar, S. Bhat, U. R. Acharya, H. Adeli, and G. M. Bairy, "Diagnosis of attention deficit hyperactivity disorder using imaging and signal processing techniques," Computers in biology and medicine, vol. 88, pp. 93-99, 2017.
  4. 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.
  5. S. Beriha, "Computer Aided Diagnosis System To Distinguish Adhd From Similar Behavioral Disorders," Biomedical & Pharmacology Journal, vol. 11, no. 2, p. 1135, 2018.
  6. Díaz-Román, R. Mitchell, and S. Cortese, "Sleep in adults with ADHD: systematic review and meta-analysis of subjective and objective studies," Neuroscience & Biobehavioral Reviews, vol. 89, pp. 61-71, 2018.
  7. Y. Karimui, S. Azadi, and P. Keshavarzi, "The ADHD effect on the actions obtained from the EEG signals," Biocybernetics and Biomedical Engineering, vol. 38, no. 2, pp. 425-437, 2018.
  8. Ghassemi, M. Hassan_Moradi, M. Tehrani-Doost, and V. Abootalebi, "Using non-linear features of EEG for ADHD/normal participants’ classification," Procedia-Social and Behavioral Sciences, vol. 32, pp. 148-152, 2012.
  9. 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.
  10. Asherson, W. Chen, B. Craddock, and E. Taylor, "Adult attention-deficit hyperactivity disorder: recognition and treatment in general adult psychiatry," The British Journal of Psychiatry, vol. 190, no. 1, pp. 4-5, 2007.
  11. Lenartowicz and S. K. Loo, "Use of EEG to diagnose ADHD," Current psychiatry reports, vol. 16, no. 11, p. 498, 2014.
  12. Tenev, S. Markovska-Simoska, L. Kocarev, J. Pop-Jordanov, A. Müller, and G. Candrian, "Machine learning approach for classification of ADHD adults," International Journal of Psychophysiology, vol. 93, no. 1, pp. 162-166, 2014.
  13. Turel and A. Bechara, "Social networking site use while driving: ADHD and the mediating roles of stress, self-esteem and craving," Frontiers in psychology, vol. 7, p. 455, 2016.
  14. C. Bledsoe et al., "Diagnostic classification of ADHD versus control: support vector machine classification using brief neuropsychological assessment," Journal of attention disorders, p. 1087054716649666, 2016.
  15. B. Saydam, H. B. AYVAŞIK, and B. Alyanak, "Executive functioning in subtypes of attention deficit hyperactivity disorder," Nöro Psikiyatri Arşivi, vol. 52, no. 4, p. 386, 2015.
  16. Lévesque, M. Beauregard, and B. Mensour, "Effect of neurofeedback training on the neural substrates of selective attention in children with attention-deficit/hyperactivity disorder: a functional magnetic resonance imaging study," Neuroscience letters, vol. 394, no. 3, pp. 216-221, 2006.
  17. M. Martel and J. T. Nigg, "Child ADHD and personality/temperament traits of reactive and effortful control, resiliency, and emotionality," Journal of Child Psychology and Psychiatry, vol. 47, no. 11, pp. 1175-1183, 2006.
  18. Wangler et al., "Neurofeedback in children with ADHD: specific event-related potential findings of a randomized controlled trial," Clinical Neurophysiology, vol. 122, no. 5, pp. 942-950, 2011.
  19. 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.
  20. -H. Park, Y. S. Kweon, S. J. Lee, E.-J. Park, C. Lee, and C.-U. Lee, "Differences in performance of ADHD children on a visual and auditory continuous performance test according to IQ," Psychiatry investigation, vol. 8, no. 3, p. 227, 2011.
  21. Y. WU, Y. S. HUANG, Y. Y. CHEN, C. K. CHEN, T. C. CHANG, and C. C. CHAO, "Psychometric study of the test of variables of attention: Preliminary findings on Taiwanese children with attention‐deficit/hyperactivity disorder," Psychiatry and clinical neurosciences, vol. 61, no. 3, pp. 211-218, 2007.
  22. Anjana, F. Khaliq, and N. Vaney, "Event-related potentials study in attention deficit hyperactivity disorder," Functional neurology, vol. 25, no. 2, p. 87, 2010.
  23. Heinrich, T. Hoegl, G. H. Moll, and O. Kratz, "A bimodal neurophysiological study of motor control in attention-deficit hyperactivity disorder: a step towards core mechanisms?," Brain, vol. 137, no. 4, pp. 1156-1166, 2014.
  24. Mueller, G. Candrian, J. D. Kropotov, V. A. Ponomarev, and G.-M. Baschera, "Classification of ADHD patients on the basis of independent ERP components using a machine learning system," in Nonlinear biomedical physics, 2010, vol. 4, no. S1, p. S1: Springer.
  25. Y. Karimui, S. Azadi, and P. Keshavarzi, "The ADHD effect on the high-dimensional phase space trajectories of EEG signals," Chaos, Solitons & Fractals, vol. 121, pp. 39-49, 2019.
  26. 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, 2011, pp. 1-4: IEEE.
  27. Khoshnoud, M. A. Nazari, and M. Shamsi, "Functional brain dynamic analysis of ADHD and control children using nonlinear dynamical features of EEG signals," Journal of integrative neuroscience, vol. 17, no. 1, pp. 17-30, 2018.
  28. Sharma and R. B. Pachori, "Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions," Expert Systems with Applications, vol. 42, no. 3, pp. 1106-1117, 2015.
  29. Yaghoobi, S. Azadi, and P. Keshavarzi, "Loss Detection of Recurrence Rate in the EEG Signals of Children with ADHD," 2019.
  30. Esmailpoor, A. M. Nasrabadi, and S. Malayeri, "An auditory brainstem response-based expert system for ADHD diagnosis using recurrence qualification analysis and wavelet support vector machine," in 2015 23rd Iranian Conference on Electrical Engineering, 2015, pp. 6-10: IEEE.
  31. Sandford and A. Turner, "Integrated visual and auditory continuous performance test manual," Richmond, VA: Braintrain Inc, 2000.
  32. Strauss, E., Sherman, E. & Spreen, O. A compendium of neuropsychological tests. (New York: Oxford University Press, 2006).
  33. Y. Karimui and S. Azadi, "Cardiac arrhythmia classification using the phase space sorted by Poincare sections," Biocybernetics and Biomedical Engineering, vol. 37, no. 4, pp. 690-700, 2017.
  34. Almasi, M. B. Shamsollahi, and L. Senhadji, "Bayesian denoising framework of phonocardiogram based on a new dynamical model," Irbm, vol. 34, no. 3, pp. 214-225, 2013.
  35. C. Hilborn, Chaos and nonlinear dynamics: an introduction for scientists and engineers. Oxford University Press on Demand, 2000.
  36. Bluschke, F. Broschwitz, S. Kohl, V. Roessner, and C. Beste, "The neuronal mechanisms underlying improvement of impulsivity in ADHD by theta/beta neurofeedback," Scientific reports, vol. 6, no. 1, pp. 1-9, 2016.
  37. W. Janssen et al., "Learning curves of theta/beta neurofeedback in children with ADHD," European child & adolescent psychiatry, vol. 26, no. 5, pp. 573-582, 2017.
  38. Van Doren et al., "Theta/beta neurofeedback in children with ADHD: feasibility of a short-term setting and plasticity effects," International Journal of Psychophysiology, vol. 112, pp. 80-88, 2017.
  39. 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.
  40. M. Lansbergen, M. Arns, M. van Dongen-Boomsma, D. Spronk, and J. K. Buitelaar, "The increase in theta/beta ratio on resting-state EEG in boys with attention-deficit/hyperactivity disorder is mediated by slow alpha peak frequency," Progress in Neuro-Psychopharmacology and Biological Psychiatry, vol. 35, no. 1, pp. 47-52, 2011.
  41. 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.
  42. Markovska-Simoska and N. Pop-Jordanova, "Quantitative EEG in children and adults with attention deficit hyperactivity disorder: comparison of absolute and relative power spectra and theta/beta ratio," Clinical EEG and neuroscience, vol. 48, no. 1, pp. 20-32, 2017.
  43. Karimu and S. Azadi, "Lossless EEG compression using the DCT and the Huffman coding," 2016.
  44. Fernández et al., "Complexity analysis of spontaneous brain activity in attention-deficit/hyperactivity disorder: diagnostic implications," Biological psychiatry, vol. 65, no. 7, pp. 571-577, 2009.
  45. L. Goldberger, C.-K. Peng, and L. A. Lipsitz, "What is physiologic complexity and how does it change with aging and disease?," Neurobiology of aging, vol. 23, no. 1, pp. 23-26, 2002.
  46. A. Lipsitz and A. L. Goldberger, "Loss of'complexity'and aging: potential applications of fractals and chaos theory to senescence," Jama, vol. 267, no. 13, pp. 1806-1809, 1992.
  47. Takahashi, "Complexity of spontaneous brain activity in mental disorders," Progress in Neuro-Psychopharmacology and Biological Psychiatry, vol. 45, pp. 258-266, 2013.
  48. A. Nazari, F. Wallois, A. Aarabi, and P. Berquin, "Dynamic changes in quantitative electroencephalogram during continuous performance test in children with attention-deficit/hyperactivity disorder," International journal of psychophysiology, vol. 81, no. 3, pp. 230-236, 2011.
  49. M. Williams et al., "Using brain-based cognitive measures to support clinical decisions in ADHD," Pediatric neurology, vol. 42, no. 2, pp. 118-126, 2010.
  50. Kamida et al., "EEG power spectrum analysis in children with ADHD," Yonago acta medica, vol. 59, no. 2, p. 169, 2016.
  51. K. Loo, A. Cho, T. S. Hale, J. McGough, J. McCracken, and S. L. Smalley, "Characterization of the theta to beta ratio in ADHD: identifying potential sources of heterogeneity," Journal of attention disorders, vol. 17, no. 5, pp. 384-392, 2013.
  52. D. Liechti et al., "Diagnostic value of resting electroencephalogram in attention-deficit/hyperactivity disorder across the lifespan," Brain topography, vol. 26, no. 1, pp. 135-151, 2013.
  53. Helgadóttir et al., "Electroencephalography as a clinical tool for diagnosing and monitoring attention deficit hyperactivity disorder: a cross-sectional study," BMJ open, vol. 5, no. 1, 2015.
  54. Ogrim, J. Kropotov, and K. Hestad, "The quantitative EEG theta/beta ratio in attention deficit/hyperactivity disorder and normal controls: sensitivity, specificity, and behavioral correlates," Psychiatry research, vol. 198, no. 3, pp. 482-488, 2012.
  55. A. Magee, A. R. Clarke, R. J. Barry, R. McCarthy, and M. Selikowitz, "Examining the diagnostic utility of EEG power measures in children with attention deficit/hyperactivity disorder," Clinical Neurophysiology, vol. 116, no. 5, pp. 1033-1040, 2005.
  56. 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.
  57. M. Snyder, H. Quintana, S. B. Sexson, P. Knott, A. Haque, and D. A. Reynolds, "Blinded, multi-center validation of EEG and rating scales in identifying ADHD within a clinical sample," Psychiatry research, vol. 159, no. 3, pp. 346-358, 2008.
  58. J. Monastra, J. F. Lubar, and M. Linden, "The development of a quantitative electroencephalographic scanning process for attention deficit–hyperactivity disorder: Reliability and validity studies," Neuropsychology, vol. 15, no. 1, p. 136, 2001.
  59. K. Boroujeni, A. A. Rastegari, and 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, 2019.
  60. Abibullaev and J. An, "Decision support algorithm for diagnosis of ADHD using electroencephalograms," Journal of medical systems, vol. 36, no. 4, pp. 2675-2688, 2012.
  61. Y. Karimu and S. Azadi, "Diagnosing the ADHD using a mixture of expert fuzzy models," International Journal of Fuzzy Systems, vol. 20, no. 4, pp. 1282-1296, 2018.
  62. Buyck and J. R. Wiersema, "Resting electroencephalogram in attention deficit hyperactivity disorder: developmental course and diagnostic value," Psychiatry research, vol. 216, no. 3, pp. 391-397, 2014.
  63. Kaur, P. Arun, S. Singh, and D. Kaur, "EEG Based Decision Support System to Diagnose Adults with ADHD," in 2018 IEEE Applied Signal Processing Conference (ASPCON), 2018, pp. 87-91: IEEE.
  64. 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.
  65. 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," Frontiers in neuroscience, vol. 14, 2020.
نشریه علمی مهندسی پزشکی زیستی
دوره 15، شماره 1
بهار 1400
صفحه 29-46

  • تاریخ دریافت 05 آبان 1399
  • تاریخ بازنگری 08 فروردین 1400
  • تاریخ پذیرش 11 فروردین 1400

صفحه اصلی

تماس با ما