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Journal of Integrative Neuroscience  2018, Vol. 17 Issue (4): 365-369    DOI: 10.31083/j.jin.2018.04.0406
Research article Previous articles | Next articles
Abnormal P50 sensory gating in schizophrenia: A permutation fuzzy entropy analysis
Wei Zhang1, 2, *(), Shuze Liu3, Jie Xiang2, Jin Li2, Aichun Qiao4
1 Information Center, Shanxi Health Vocational College, Taiyuan 030012, P. R. China
2 College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, P. R. China
3 Department of Computer Science, Rensselaer Polytechnic Institute, Troy, New York 12180-3590, USA
4 Department of General Practice, Shanxi Academy of Medical Sciences, Shanxi Dayi Hospital, Taiyuan 030032, P. R. China
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A permutation fuzzy entropy algorithm is proposed that uses sorting and symbolic methods to improve anti-noise performance of electroencephalogram signals known to be highly sensitive to noise disturbances during collection. It was employed to analyse abnormal event-related potentials of schizophrenics focused on P50 potentials of sensory gating, which is the most common paradigm currently used for analysing schizophrenia. The approach for analysing P50 sensory gating in schizophrenics is presented from twenty-seven schizophrenia patients and twenty healthy controls. The values calculated for the patients under the conditioning and testing stimuli were used to calculate the entropy complexity. Results demonstrate that the approach can be effectively used to analyse sensory gating deficits in patients with schizophrenia and that the algorithm can be satisfactorily be used for analysing electroencephalogram signals.

Key words:  Permutation fuzzy entropy      P50 sensory gating      electroencephalogography      schizophrenia      event related potential     
Submitted:  15 November 2017      Accepted:  20 December 2017      Published:  15 November 2018     
*Corresponding Author(s):  Wei Zhang     E-mail:

Cite this article: 

Wei Zhang, Shuze Liu, Jie Xiang, Jin Li, Aichun Qiao. Abnormal P50 sensory gating in schizophrenia: A permutation fuzzy entropy analysis. Journal of Integrative Neuroscience, 2018, 17(4): 365-369.

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Fig. 1.  Steps of preprocessing ERP data using Analyzer software. Raw data can be pretreated in the work flow.

Fig. 2.  Position of the 11 selected electrodes. Channels were located in different brain regions.

Fig. 3.  (a) Mean PFEN plot for control subjects under stimuli S1 and S2. The black and grey plots give the entropy curve under S1 and S2, respectively. (b) PFEN mean plots of test subjects in response to S1 and S2 stimulation. The black and grey plots give the entropy under S1 and S2, respectively.

Table 1  Statistical Test Results of Entropy Analysis of Schizophrenia Data
Channel (electrode position) F3 Fz F4 T7 C3 Cz
Normal PFEN 0.0002 0.0001 0.0022 0.0051 0.0012 0.0001
p value PE 0.7938 0.0072 0.2043 0.2959 0.2180 0.3507
FuzzyEn 0.0859 0.1560 0.0304 0.0057 0.0859 0.8519
PFEN *** *** ** ** ** ***
Significance PE **
FuzzyEn **
Schizophrenia PFEN 0.1864 0.0754 0.0163 0.5322 0.0837 0.3130
p value PE 0.4279 0.9234 0.7731 0.8101 0.1075 0.7007
FuzzyEn 0.1075 0.5642 0.0186 0.8288 0.5971 0.8664
Significance PE
Table 2  Statistical Test Results of Entropy Analysis for Schizophrenia Data (continued Table)
Channel (electrode position) C4 P3 Pz P4 T8
Normal PFEN 0.0003 0.0001 0.0001 0.0010 0.0051
p value PE 0.1084 0.0333 0.0017 0.4115 0.7369
FuzzyEn 0.0930 1.0000 0.9405 0.3135 0.4330
PFEN *** *** *** ** **
Significance PE **
Schizophrenia PFEN 0.0411 0.4279 0.4711 0.4140 0.0679
p value PE 0.0306 0.5165 0.6480 0.7916 0.7548
FuzzyEn 0.9044 0.2488 0.0643 0.1563 0.9808
Significance PE
[1] Martis RJ, Tan JH, Chua CK, Loon TC, YEO SWJ, Tong L ( 2015) Epileptic EEG classification using nonlinear parameters on different frequency bands. Journal of Mechanics in Medicine and Biology 15(03), 1550040.
doi: 10.1142/S0219519415500402
[2] Acharya UR, Sudarshan VK, Adeli H, Santhosh J, Koh JE, Adeli A ( 2015) Computer-aided diagnosis of depression using EEG signals. European Neurology 73(5,6), 329-336.
doi: 10.1159/000381950 pmid: 25997732
[3] Janjarasjitt S, Loparo KA ( 2014) Scale-invariant behavior of epileptic ECoG. Journal of Medical Biology Engineering 34(6), 535-541.
[4] Gómez C, Poza J, Gutiérrez MT, Prada E, Mendoza N, Hornero R ( 2016) Characterization of EEG patterns in brain-injured subjects and controls after a Snoezelen intervention. Computer Methods and Programs in Biomedicine 136, 1-9.
doi: 10.1016/j.cmpb.2016.08.008 pmid: 27686698
[5] Wang L, Long X, Arends JB, Aarts RM ( 2017) EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures. Journal of Neuroscience Methods 290, 85-94.
doi: 10.1016/j.jneumeth.2017.07.013 pmid: 28734799
[6] Kannathal N, Choo ML, Acharya UR, Sadasivan P ( 2005) Entropies for detection of epilepsy in EEG. Computer Methods and Programs in Biomedicine 80(3), 187-194.
doi: 10.1016/j.cmpb.2005.06.012 pmid: 16219385
[7] Jie X, Cao R, Li L ( 2014) Emotion recognition based on the sample entropy of EEG. Bio-medical Materials and Engineering 24(1), 1185-1192.
doi: 10.3233/BME-130919 pmid: 24212012
[8] Cuesta-Frau D, Miró-Martínez P, Núñez JJ, Oltra-Crespo S, Picó AM ( 2017) Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics. Computers in Biology and Medicine 87, 141-151.
doi: 10.1016/j.compbiomed.2017.05.028 pmid: 28595129
[9] Song Y, Zhang J ( 2016) Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine. Journal of Neuroscience Methods 257, 45-54.
doi: 10.1016/j.jneumeth.2015.08.026 pmid: 26335801
[10] Pincus SM ( 1991) Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences of the United States of America 88(6), 2297-2301.
[11] Richman JS, Moorman JR ( 2000) Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology 278(6), H2039-H2049.
doi: 10.1152/ajpheart.2000.278.6.H2039 pmid: 10843903
[12] Chen W, Wang Z, Xie H, Yu W ( 2007) Characterization of surface EMG signal based on fuzzy entropy. IEEE Transactions on Neural Systems and Rehabilitation Engineering 15(2), 266-272.
doi: 10.1109/TNSRE.2007.897025 pmid: 17601197
[13] Bandt C, Pompe B ( 2002) Permutation entropy: a natural complexity measure for time series. Physical Review Letters 88(17), 174102.
doi: 10.1103/PhysRevLett.88.174102 pmid: 12005759
[14] Li J, Yan J, Liu X, Ouyang G ( 2014) Using permutation entropy to measure the changes in EEG signals during absence seizures. Entropy 16(6), 3049-3061.
doi: 10.3390/e16063049
[15] Cui D, Wang J, Bian Z, Li Q, Wang L, Li X ( 2015) Analysis of entropies based on empirical mode decomposition in amnesic mild cognitive impairment of diabetes mellitus. Journal of Innovative Optical Health Sciences 8(05), 1550010.
doi: 10.1142/S1793545815500108
[16] Mateos D, Diaz JM, Lamberti PW ( 2014) Permutation entropy applied to the characterization of the clinical evolution of epileptic patients under pharmacologicaltreatment. Entropy 16(11), 5668-5676.
doi: 10.3390/e16115668
[17] Olofsen E, Sleigh J, Dahan A ( 2008) Permutation entropy of the electroencephalogram: a measure of anaesthetic drug effect. British Journal of Anaesthesia 101(6), 810-821.
doi: 10.1093/bja/aen290 pmid: 18852113
[18] Sharma R, Pachori RB, Acharya UR ( 2015) An integrated index for the identification of focal electroencephalogram signals using discrete wavelet transform and entropy measures. Entropy 17(8), 5218-5240.
doi: 10.3390/e17085218
[19] Eßlinger M, Wachholz S, Manitz M-P, Plümper J, Sommer R, Juckel G, Friebe A ( 2016) Schizophrenia associated sensory gating deficits develop after adolescent microglia activation. Brain, Behavior, and Immunity 58, 99-106.
doi: 10.1016/j.bbi.2016.05.018 pmid: 27235930
[20] Mao Q, Tan YL, Luo XG, Tian L, Wang ZR, Tan SP, Chen S, Yang GG, An HM, Yang FD ( 2016) Association of catechol-O-methyltransferase Val108/158 Met genetic polymorphism with schizophrenia, P50 sensory gating, and negative symptoms in a Chinese population. Psychiatry Research 242, 271-276.
doi: 10.1016/j.psychres.2016.04.029 pmid: 27315458
[21] Demily C, Louchart-de-la-Chapelle S, Nkam I, Ramoz N, Denise P, Nicolas A, Savalle C, Thibaut F ( 2016) Does COMT val158met polymorphism influence P50 sensory gating, eye tracking or saccadic inhibition dysfunctions in schizophrenia? Psychiatry Research 246, 738-744.
doi: 10.1016/j.psychres.2016.07.066 pmid: 27825784
[22] Hashimoto K, Iyo M, Freedman R, Stevens KE ( 2005) Tropisetron improves deficient inhibitory auditory processing in DBA/2 mice: role of α 7 nicotinic acetylcholine receptors . Psychopharmacology 183(1), 13-19.
doi: 10.1007/s00213-005-0142-0 pmid: 16136299
[23] Smith AK, Edgar JC, Huang M, Lu BY, Thoma RJ, Hanlon FM, McHaffie G, Jones AP, Paz RD, Miller GA ( 2010) Cognitive abilities and 50-and 100-msec paired-click processes in schizophrenia. American Journal of Psychiatry 167(10), 1264-1275.
doi: 10.1176/appi.ajp.2010.09071059 pmid: 20634366
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