Time-Frequency Distribution Analysis for Electroencephalogram Signals of Patients With Schizophrenia and Normal Participants
International Clinical Neuroscience Journal,
Vol. 9 (2022),
,
Page e11
Abstract
Background: Psychiatrists diagnose schizophrenia based on clinical symptoms such as disordered thinking, delusions, hallucinations, and severe distortion of daily functions. However, some of these symptoms are common with other mental illnesses such as bipolar mood disorder. Therefore, quantitative assessment of schizophrenia by analyzing a physiological-based data such as the electroencephalogram (EEG) signal is of interest. In this study, we analyze the spectrum and time-frequency distribution (TFD) of EEG signals to understand how schizophrenia affects these signals.
Methods: In this regard, EEG signals of 20 patients with schizophrenia and 20 age-matched participants (control group) were investigated. Several features including spectral flux, spectral flatness, spectral entropy, time-frequency (TF)-flux, TF-flatness, and TF-entropy were extracted from the EEG signals.
Results: Spectral flux (1.5388 ± 0.0038 and 1.5497 ± 0.0058 for the control and case groups, respectively, P = 0.0000), spectral entropy (0.8526 ± 0.0386 and 0.9018 ± 0.0428 for the control and case groups, respectively, P = 0.0004), spectral roll-off (0.3896 ± 0.0434 and 0.4245 ± 0.0410 for the control and case groups, respectively, P = 0.0129), spectral flatness (0.1401 ± 0.0063 and 0.1467 ± 0.0077 for the control and case groups, respectively, P = 0.0055), TF-flux (1.2675 ± 0.1806 and 1.5284 ± 0.2057 for the control and case groups, respectively, P = 0.0001) and TF-flatness (0.9980 ± 0.0000 and 0.9981 ± 0.0000 for the control and case groups, respectively, P = 0.0000) values in patients with schizophrenia were significantly greater than the control group in most EEG channels. This prominent irregularity may be caused by decreasing the synchronization of neurons in the frontal lobe.
Conclusion: Spectral and time frequency distribution analysis of EEG signals can be used as quantitative indexes for neurodynamic investigation in schizophrenia.
- EEG signal classification, Spectral, Time-frequency distribution
How to Cite
References
American Psychiatric Association (APA). Diagnostic and Statistical Manual of Mental Disorders: DSM-5. United States: APA; 2013.
Boostani R, Sabeti M. Can evolutionary-based brain map be used as a complementary diagnostic tool with fMRI, CT and PET for schizophrenic patients? J Biomed Phys Eng. 2017;7(2):169-80.
Taghavi M, Boostani R, Sabeti M, Taghavi SM. Usefulness of approximate entropy in the diagnosis of schizophrenia. Iran J Psychiatry Behav Sci. 2011;5(2):62-70.
Schomer DL, Lopes da Silva FH. Niedermeyer’s Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins; 2012.
Parvinnia E, Sabeti M, Zolghadri Jahromi M, Boostani R. Classification of EEG signals using adaptive weighted distance nearest neighbor algorithm. J King Saud Univ Comput Inf Sci. 2014;26(1):1-6. doi: 10.1016/j.jksuci.2013.01.001.
Boostani R, Sadatnezhad K, Sabeti M. An efficient classifier to diagnose of schizophrenia based on the EEG signals. Expert Syst Appl. 2009;36(3 Pt 2):6492-9. doi: 10.1016/j. eswa.2008.07.037.
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(6):1232-41. doi: 10.1016/j. clinph.2008.01.104.
Zamani J, Bonyadi Naeini A. Best feature extraction and classification algorithms for EEG signals in neuromarketing. Front Biomed Technol. 2020;7(3):186-91. doi: 10.18502/fbt. v7i3.4621.
Fiscon G, Weitschek E, Cialini A, Felici G, Bertolazzi P, De Salvo S, et al. Combining EEG signal processing with supervised methods for Alzheimer’s patients classification. BMC Med Inform Decis Mak. 2018;18(1):35. doi: 10.1186/ s12911-018-0613-y.
Alimardani F, Boostani R. DB-FFR: a modified feature selection algorithm to improve discrimination rate between bipolar mood disorder (BMD) and schizophrenic patients. Iran J Sci Technol Trans Electr Eng. 2018;42(3):251-60. doi: 10.1007/s40998-018-0060-x.
Sabeti M, Boostani R, Katebi SD, Price GW. Selection of relevant features for EEG signal classification of schizophrenic patients. Biomed Signal Process Control. 2007;2(2):122-34. doi: 10.1016/j.bspc.2007.03.003.
Sabeti M, Katebi SD, Boostani R, Price GW. A new approach for EEG signal classification of schizophrenic and control participants. Expert Syst Appl. 2011;38(3):2063-71. doi: 10.1016/j.eswa.2010.07.145.
Fattahi D, Nasihatkon B, Boostani R. A general framework to estimate spatial and spatio-spectral filters for EEG signal classification. Neurocomputing. 2013;119:165-74. doi: 10.1016/j.neucom.2013.03.044.
Amin HU, Mumtaz W, Subhani AR, Mohamad Saad MN, Malik AS. Classification of EEG signals based on pattern recognition approach. Front Comput Neurosci. 2017;11:103. doi: 10.3389/fncom.2017.00103.
Nanthini BS, Santhi B. Electroencephalogram signal classification for automated epileptic seizure detection using
genetic algorithm. J Nat Sci Biol Med. 2017;8(2):159-66. doi: 10.4103/jnsbm.JNSBM_285_16.
Sabeti M, Boostani R, Moradi E. Event related potential (ERP) as a reliable biometric indicator: a comparative approach. Array. 2020;6:100026. doi: 10.1016/j.array.2020.100026.
Sabeti M, Boostani R, Zoughi T. Using genetic programming to select the informative EEG-based features to distinguish schizophrenic patients. Neural Netw World. 2012;22(1):3-20. doi: 10.14311/nnw.2012.22.001.
Ciprian C, Masychev K, Ravan M, Manimaran A, Deshmukh A. Diagnosing schizophrenia using effective connectivity of resting-state EEG data. Algorithms. 2021;14(5):139. doi: 10.3390/a14050139.
Sun J, Cao R, Zhou M, Hussain W, Wang B, Xue J, et al. A hybrid deep neural network for classification of schizophrenia using EEG data. Sci Rep. 2021;11(1):4706. doi: 10.1038/ s41598-021-83350-6.
Kim K, Duc NT, Choi M, Lee B. EEG microstate features for schizophrenia classification. PLoS One. 2021;16(5):e0251842. doi: 10.1371/journal.pone.0251842.
Prabhakar SK, Rajaguru H, Kim SH. Schizophrenia EEG signal classification based on swarm intelligence computing. Comput Intell Neurosci. 2020;2020:8853835. doi: 10.1155/2020/8853835.
Sabeti M, Katebi S, Boostani R. Entropy and complexity measures for EEG signal classification of schizophrenic and control participants. Artif Intell Med. 2009;47(3):263-74. doi: 10.1016/j.artmed.2009.03.003.
Kutepov IE, Dobriyan VV, Zhigalov MV, Stepanov MF, Krysko AV, Yakovleva TV, et al. EEG analysis in patients with schizophrenia based on Lyapunov exponents. Inform Med Unlocked. 2020;18:100289. doi: 10.1016/j. imu.2020.100289.
Boostani R, Sabeti M. Optimising brain map for the diagnosis of schizophrenia. Int J Biomed Eng Technol. 2018;28(2):105- 19. doi: 10.1504/ijbet.2018.094728.
Alimardani F, Cho J, Boostani R, Hwang H. Classification of bipolar disorder and schizophrenia using steady-state visual evoked potential based features. IEEE Access. 2018;6:40379- 88. doi: 10.1109/ACCESS.2018.2854555.
World Health Organization (WHO). The International Statistical Classification of Diseases and Health Related Problems ICD-10: Tenth Revision. Volume 1: Tabular List. WHO; 2004.
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(6):695-703. doi: 10.1111/j.1469-8986.1986.tb00696.x.
Melia U, Claria F, Vallverdu M, Caminal P. Measuring instantaneous and spectral information entropies by Shannon entropy of Choi-Williams distribution in the context of electroencephalography. Entropy. 2014;16(5):2530-48. doi: 10.3390/e16052530.
Sucic V, Saulig N, Boashash B. Estimating the number of components of a multicomponent nonstationary signal using the short-term time-frequency Rényi entropy. EURASIP J Adv Signal Process. 2011;2011(1):125. doi: 10.1186/1687-6180- 2011-125.
Mohammadi M, Khan NA, Pouyan AA. Automatic seizure detection using a highly adaptive directional time– frequency distribution. Multidimens Syst Signal Process. 2018;29(4):1661-78. doi: 10.1007/s11045-017-0522-8.
Mohammadi M, Pouyan AA, Khan NA, Abolghasemi V. Locally optimized adaptive directional time–frequency distributions. Circuits Syst Signal Process. 2018;37(8):3154- 74. doi: 10.1007/s00034-018-0802-z.
Boashash B. Time-Frequency Signal Analysis and Processing: A Comprehensive Reference. Academic Press; 2015.
Stanković L. A measure of some time–frequency distributions concentration. Signal Process. 2001;81(3):621-31. doi: 10.1016/s0165-1684(00)00236-x.
Sanei S, Chambers JA. EEG Signal Processing. John Wiley & Sons; 2013.
Koukkou M, Lehmann D, Wackermann J, Dvorak I, Henggeler B. Dimensional complexity of EEG brain mechanisms in untreated schizophrenia. Biol Psychiatry. 1993;33(6):397- 407. doi: 10.1016/0006-3223(93)90167-c.
Andreasen NC, O’Leary DS, Flaum M, Nopoulos P, Watkins GL, Boles Ponto LL, et al. Hypofrontality in schizophrenia: distributed dysfunctional circuits in neuroleptic-naïve patients. Lancet. 1997;349(9067):1730-4. doi: 10.1016/ s0140-6736(96)08258-x.
Liemburg EJ, Knegtering H, Klein HC, Kortekaas R, Aleman A. Antipsychotic medication and prefrontal cortex activation: a review of neuroimaging findings. Eur Neuropsychopharmacol. 2012;22(6):387-400. doi: 10.1016/j.euroneuro.2011.12.008.
Mubarik A, Tohid H. Frontal lobe alterations in schizophrenia: a review. Trends Psychiatry Psychother. 2016;38(4):198-206. doi: 10.1590/2237-6089-2015-0088.
Carlino E, Sigaudo M, Rosato R, Vighetti S, Rocca P. Electroencephalographic connectivity analysis in schizophrenia. Neurosci Lett. 2015;604:145-50. doi: 10.1016/j.neulet.2015.07.045.
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