Study of Metrics that Could be Considered as Inputs to an Intelligent System for Diagnosing Schizophrenia based on an Electroencephalogram (EEG)

##plugins.themes.bootstrap3.article.main##

  •   Pélagie Flore Temgoua Nanfack

  •   Angouah Massaga Morel Junior

Abstract

Intelligent systems are now part of human daily life. This justifies its application in various fields. However, the field of psychiatry still seems to be at a disadvantage. In this paper, we are interested in measures that can be used as input to an intelligent system for detecting brain diseases using an electroencephalogram (EEG). Indeed, spectral analysis and the study of brain connectivity are two methods of EEG analysis that can be used to characterize schizophrenia. The spectral analysis allows to calculate the power (absolute and relative), the frequency peak among others. Concerning the study of cerebral connectivity, the Phase Lag Index (PLI), which is an adjacency matrix ; which is an adjacency matrix used to assess brain connectivity. Once the PLI is obtained, units such as degree, density and strength on each channel are calculated. These units are evaluated on twenty (28) EEGs, fourteen (14) of people suffering from schizophrenia and fourteen (14) of healthy people. Once the PLI is obtained, units such as degree, density and strength on each channel are calculated. These units are evaluated on twenty (28) EEGs, fourteen (14) of people suffering from schizophrenia and fourteen (14) of healthy people. On the other hand, the value of strength is always lower in sick people than in healthy people. This is true regardless of the frequency band or channel used. This study shows that values such as degree, density, strength of a predefined adjacency matrix, then power, peak frequency band can be used as input values of an intelligent system for diagnosing psychiatric or brain diseases such as schizophrenia.


Keywords: EEG, Schizophrenia, PLI, Strength, Degree, Density, ANOVA

References

A. américaine de psychiatrie, Manuel diagnostique et statistique destroubles mentaux, MASSON, 1952.URLhttps://psychiatrieweb.files.wordpress.com/2011/12/manuel-diagnostique-troubles-mentaux.pdf

W. H. Organization, W. H. Organization (Eds.), Mental health atlas2017, World Health Organization, Geneva, Switzerland, 2018, oCLC :on1053843822.

B. régional de l’Organisation mondiale de la Santé pour l’Afrique,Activités de l’OMS dans la région Africaine: Rapport biennal de laDirectrice régionale.URL https://apps.who.int/iris/handle/10665/260407

Schizophrénie.URL https://www.who.int/fr/news-room/fact-sheets/detail/schizophrenia

E. Olejarczyk, W. Jernajczyk, Graph-based analysis of brain connectivityin schizophrenia, PLOS ONE 12 (11) (November 2017).doi:https://doi.org/10.1371/journal.pone.0188629.

Han, Shaoqiang, Wang, Yifeng, L. Wei, Duan, Xujun, Guo, Jing,YuYangyang, Ye, Liangkai, LiJiao, Chen, Xiaogang, Huafu, The distin-guishing intrinsic brain circuitry in treatment-naïve first-episode schizo-phrenia : Ensemble learning classification, Neurocomputing 365 (No-vember 2019).doi:https://doi.org/10.1016/j.neucom.2019.07.061.

A. Muselle, M. Desseilles, Utilité de la neuroimagerie en psychiatrie 9.

D. Linden, Neuroimaging and Neurophysiology in Psychiatry, OxfordUniversity Press, 2016.doi:10.1093/med/9780198739609.001.0001.URLhttp://oxfordmedicine.com/view/10.1093/med/9780198739609.001.0001/med-9780198739609

Mana, J.-L. Martinot, Stéphanie, La neuro-imagerie de la psychiatrie àla pédopsychiatrie imagerie et cognition, médecine/sciences – Inserm/ SRMS 27 (July 2011).doi:https://doi.org/10.1051/medsci/2011276017.[10] R.-A. Salido-Ruiz, Problèmes inverses contraints en eeg : applicationsauxvpotentiels absolus et l’influence du signal de référence dans l’ana-lyse de l’eeg, Ph.D. thesis, Université Toulouse III – Paul Sabatier (022008).

T.-S. Yeum, U. G. Kang, Reduction in alpha peak frequency andcoherence on quantitative electroencephalography in patients with schi-zophrenia, Journal of Korean Medical Science 33 (May 2018).doi:10.3346/jkms.2018.33.e179.

T. Takahashi, T. Goto, S. Nobukawa, Y. Tanaka, M. Kikuchi, M. Higa-shima, Y. Wadab, Abnormal functional connectivity of high-frequencyrhythms in drug-naïve schizophrenia 33 (November 2011).doi:10.1016/j.clinph.2017.11.004.

S. Treserras, Etudes sur la connectivité cérébrale : aspects méthodolo-giques et applications au cerveau au repos, à la motricité et à la lecture,Ph.D. thesis, Université de Lorraine (07 2012).

G. Adrian, Computationally characterizing schizophrenia (2012).

C. J. Stam, G. Nolte, A. Daffertshofer, Phase lag index : Assessment offunctional connectivity from multi channel eeg and meg with diminishedbias from common sources, Wiley-Liss, Inc. 28 (November 2007).doi:10.1002/hbm.20346.

T. Yan, W. Wang, T. Liu, D. Chen, Y. L. Changming Wang, X. Ma,X. Tang, J. Wu, Y. Deng, L. Zhao, Increased local connectivity of brainfunctional networks during facial processing in schizophrenia : evidencefrom eeg data, impactjournals :oncotarget (2017).doi:10.18632/oncotarget.20598.

J. K. Wynn, B. J. Roach, J. Lee, W. P. Horan, J. M., Eeg findingsof reduced neural synchronization during visual integration in schi-zophrenia (2015).doi:https://doi.org/10.1371/journal.pone.0119849.

J. R. Foucher, L’intégration fonctionnelle cérébrale dans la schizophré-nie, Ph.D. thesis, Université Louis Pasteur (Strasbourg) (12 2007).

Setting the EEG reference.URLhttps://mne.tools/stable/auto_tutorials/preprocessing/plot_55_setting_eeg_reference.html

Python : tout savoir sur le principal langage Big Data et MachineLearning.URL https://www.lebigdata.fr/python-langage-definition

Downloads

Download data is not yet available.

##plugins.themes.bootstrap3.article.details##

How to Cite
[1]
Temgoua Nanfack, P.F. and Junior, A.M. 2020. Study of Metrics that Could be Considered as Inputs to an Intelligent System for Diagnosing Schizophrenia based on an Electroencephalogram (EEG). European Journal of Engineering Research and Science. 5, 3 (Mar. 2020), 292-296. DOI:https://doi.org/10.24018/ejers.2020.5.3.1811.