LinkedIn

 آمار

تعداد نشریات20
تعداد شماره‌ها1,149
تعداد مقالات10,518
تعداد مشاهده مقاله45,416,078
تعداد دریافت فایل اصل مقاله11,291,727
Boostani, R, Sabeti, M. (1396). Can Evolutionary-based Brain Map Be Used as a Complementary Diagnostic Tool with fMRI, CT and PET for Schizophrenic Patients?. سامانه مدیریت نشریات علمی, 7(2), 169-180.
R Boostani; M Sabeti. "Can Evolutionary-based Brain Map Be Used as a Complementary Diagnostic Tool with fMRI, CT and PET for Schizophrenic Patients?". سامانه مدیریت نشریات علمی, 7, 2, 1396, 169-180.
Boostani, R, Sabeti, M. (1396). 'Can Evolutionary-based Brain Map Be Used as a Complementary Diagnostic Tool with fMRI, CT and PET for Schizophrenic Patients?', سامانه مدیریت نشریات علمی, 7(2), pp. 169-180.
Boostani, R, Sabeti, M. Can Evolutionary-based Brain Map Be Used as a Complementary Diagnostic Tool with fMRI, CT and PET for Schizophrenic Patients?. سامانه مدیریت نشریات علمی, 1396; 7(2): 169-180.

Can Evolutionary-based Brain Map Be Used as a Complementary Diagnostic Tool with fMRI, CT and PET for Schizophrenic Patients?

Journal of Biomedical Physics and Engineering
مقاله 10، دوره 7، شماره 2، شهریور 2017، صفحه 169-180    اصل مقاله (1019.53 K)
نوع مقاله: Original Research
نویسندگان
R Boostani* 1؛ M Sabeti2
1Department of Computer Sciences and Engineering, School of Engineering, Shiraz University, Shiraz, Iran
2Department of Computer Engineering, College of Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
چکیده
Objective: In this research, a new approach termed as “evolutionary-based brain map” is presented as a diagnostic tool to classify schizophrenic and control subjects by distinguishing their electroencephalogram (EEG) features.Methods: Particle swarm optimization (PSO) is employed to find discriminative frequency bands from different EEG channels. By deploying the energy of those selected frequency bands from different channels within each time frame (window) on the scalp geometry, a sort of two dimensional points along with their values are created; by applying Lagrange interpolation, an image can be constructed. Finally, by averaging the images belonging to successive time frames, an evolutionary-based brain map is created.Results: In this study, twenty subjects from each group voluntarily participated and their EEG signals were caught from 20 channels. The energy of selected bands for different channels are arranged in a feature vector for each time frame and applied to Fisher linear discriminant analysis (FLDA) resulting in 83.74% diagnostic accuracy between the two groups. The achieved result by the proposed method was much higher than applying the energy of standard EEG bands (delta, theta, alpha, beta and gamma) to the same classifier which just provided 77.04% accuracy. Applying T-test to the achieved results supports the supremacy of the proposed method as an automatic powerful diagnostic tool.Conclusion: The proposed brain map is capable of highlighting the same physiological and anatomical changes which are observed in fMRI, PET and CT as differentiable indicators between controls and schizophrenic patients
کلیدواژه‌ها
EEG Classification؛ Schizophrenia Disorder؛ Band Power؛ PSO
مراجع
  1. Association AP. Diagnostic and statistical manual of mental disorders (DSM). Washington, DC: American psychiatric association; 1994. p. 143-7.
  2. Organization WH. International statistical classification of diseases and health related problems (The) ICD-10: World Health Organization; 2004.
  3. Cabeza R, Kingstone A. Handbook of functional neuroimaging of cognition. Mit Press; 2006.
  4. Ulmer S, Jansen O. fMRI: basics and clinical application. Springer; 2010.
  5. Hsieh J, editor. Computed tomography: principles, design, artifacts, and recent advances. SPIE Bellingham, WA; 2009.
  6. Niedermeyer E, da Silva FL. Electroencephalography: basic principles, clinical applications, and related fields. Lippincott Williams & Wilkins; 2005.
  7. Sadock BJ. Kaplan & Sadock’s comprehensive textbook of psychiatry: lippincott Williams & wilkins Philadelphia, PA; 2000.
  8. Shenton ME, Dickey CC, Frumin M, McCarley RW. A review of MRI findings in schizophrenia. Schizophr Res. 2001;49:1-52. doi.org/10.1016/S0920-9964(01)00163-3. PubMed PMID: 11343862. PubMed PMCID: 2812015.
  9. Boostani R, Graimann B, Moradi MH, Pfurtscheller G. A comparison approach toward finding the best feature and classifier in cue-based BCI. Med Biol Eng Comput. 2007;45:403-12. doi.org/10.1007/s11517-007-0169-y. PubMed PMID: 17318660.
  10. Schreuder M. Towards efficient auditory BCI through optimized paradigms and methods. epubli; 2014.
  11. Hoyer D, Bauer R, Conrad K, Galicki M, Doring A, Hoyer H, et al. Specific monitoring of neonatal brain function with optimized frequency bands. IEEE Eng Med Biol Mag. 2001;20:40-6. doi.org/10.1109/51.956818. PubMed PMID: 11668895.
  12. Jaffe RS, Fung DL, Behrman KH. Optimal frequency ranges for extracting information on autonomic activity from the heart rate spectrogram. J Auton Nerv Syst. 1994;46:37-46. doi.org/10.1016/0165-1838(94)90142-2. PubMed PMID: 8120342.
  13. Sabeti M, Boostani R, Katebi S, Price G. Selection of relevant features for EEG signal classification of schizophrenic patients. Biomedical Signal Processing and Control. 2007;2:122-34. doi.org/10.1016/j.bspc.2007.03.003.
  14. Sabeti M, Boostani R, Katebi S, editors. A New approach to classify the schizophrenic and normal subjects by finding the best channels and frequency bands. 2007 15th International Conference on Digital Signal Processing; 2007: IEEE.
  15. Boostani R, Sadatnezhad K, Sabeti M. An efficient classifier to diagnose of schizophrenia based on the EEG signals. Expert Systems with Applications. 2009;36:6492-9. doi.org/10.1016/j.eswa.2008.07.037.
  16. Li Y, Tong S, Liu D, Gai Y, Wang X, Wang J, et al. Abnormal EEG complexity in patients with schizophrenia and depression. Clin Neurophysiol. 2008;119:1232-41. doi.org/10.1016/j.clinph.2008.01.104. PubMed PMID: 18396454.
  17. Sabeti M, Katebi S, Boostani R. Entropy and complexity measures for EEG signal classification of schizophrenic and control participants. Artif Intell Med. 2009;47:263-74. doi.org/10.1016/j.artmed.2009.03.003. PubMed PMID: 19403281.
  18. Semlitsch HV, Anderer P, Schuster P, Presslich O. A solution for reliable and valid reduction of ocular artifacts, applied to the P300 ERP. Psychophysiology. 1986;23:695-703. doi.org/10.1111/j.1469-8986.1986.tb00696.x. PubMed PMID: 3823345.
  19. Wang X, Yang J, Teng X, Xia W, Jensen R. Feature selection based on rough sets and particle swarm optimization. Pattern Recognition Letters. 2007;28:459-71. doi.org/10.1016/j.patrec.2006.09.003.
  20. Shi Y, editor. Particle swarm optimization: developments, applications and resources. evolutionary computation, 2001. Proceedings of the 2001 Congress on; 2001: IEEE.
  21. Whitley D. A genetic algorithm tutorial. Statistics and computing. 1994;4:65-85. doi.org/10.1007/BF00175354.
  22. Dorigo M, Maniezzo V, Colorni A. Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B Cybern. 1996;26:29-41. doi.org/10.1109/3477.484436. PubMed PMID: 18263004.
  23. Socha K, Dorigo M. Ant colony optimization for continuous domains. European journal of operational research. 2008;185:1155-73. doi.org/10.1016/j.ejor.2006.06.046.
  24. Galka A. Topics in nonlinear time series analysis: with implications for EEG analysis. World Scientific; 2000.
  25. Webb AR. Statistical pattern recognition. John Wiley & Sons; 2003.
  26. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004;134:9-21. doi.org/10.1016/j.jneumeth.2003.10.009. PubMed PMID: 15102499.
  27. Foucher JR, Vidailhet P, Chanraud S, Gounot D, Grucker D, Pins D, et al. Functional integration in schizophrenia: too little or too much? Preliminary results on fMRI data. Neuroimage. 2005;26:374-88. doi.org/10.1016/j.neuroimage.2005.01.042. PubMed PMID: 15907297.
  28. Hayashi T, Suga H, Hotta N, Andoh T, Ohara M. Brain imaging approach to atypical psychoses. Biological Psychiatry. 1997;42:195S.
  29. Ragland JD, Gur RC, Raz J, Schroeder L, Kohler CG, Smith RJ, et al. Effect of schizophrenia on frontotemporal activity during word encoding and recognition: a PET cerebral blood flow study. Am J Psychiatry. 2001;158:1114-25. doi.org/10.1176/appi.ajp.158.7.1114. PubMed PMID: 11431234. PubMed PMCID: 4332582.
  30. Gur RE, McGrath C, Chan RM, Schroeder L, Turner T, Turetsky BI, et al. An fMRI study of facial emotion processing in patients with schizophrenia. Am J Psychiatry. 2002;159:1992-9. doi.org/10.1176/appi.ajp.159.12.1992. PubMed PMID: 12450947.
  31. Illowsky BP, Juliano DM, Bigelow LB, Weinberger DR. Stability of CT scan findings in schizophrenia: results of an 8 year follow-up study. J Neurol Neurosurg Psychiatry. 1988;51:209-13. doi.org/10.1136/jnnp.51.2.209. PubMed PMID: 3346684. PubMed PMCID: 1031532.
  32. Staal WG, Hulshoff Pol HE, Schnack HG, Hoogendoorn ML, Jellema K, Kahn RS. Structural brain abnormalities in patients with schizophrenia and their healthy siblings. Am J Psychiatry. 2000;157:416-21. doi.org/10.1176/appi.ajp.157.3.416. PubMed PMID: 10698818.
  33. Hulshoff Pol HE, Schnack HG, Bertens MG, van Haren NE, van der Tweel I, Staal WG, et al. Volume changes in gray matter in patients with schizophrenia. Am J Psychiatry. 2002;159:244-50. doi.org/10.1176/appi.ajp.159.2.244. PubMed PMID: 11823266.
آمار
تعداد مشاهده مقاله: 496
تعداد دریافت فایل اصل مقاله: 160


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب