Feature Extraction from Distributions of Phase Synchronization Values of EEG Recordings


  • Jaime Arturo Quirarte Tejeda Universidad de Guadalajara
  • Jorge Luis Flores Nuñez Universidad de Guadalajara
  • Rebeca Romo Vázquez Universidad de Guadalajara


Epilepsy, Phase analysis, Synchronization, Phase differences


Epilepsy is the most common neurological pathology. Despite treatments available to patients only 58% to 73% will be free of seizures. This uncertainty of the treatment’s outcome is the basis of other psychiatric affections to patients who are uncertain of the success of their treatment. Seizure prediction models (SPMs) emerged as an aid to help the patient know if he is susceptible to an imminent crisis; such models are based of continuous monitoring of EEG signals of the patient and subsequent continuous analysis of those signals. Looking for features in the signals which differentiate ictal from interictal is an ongoing field of research which aims to get a robust set of features to feed the SPM and get a high degree of certainty of when the next seizure will occur. In this work we propose the analysis of phase differences of EEG as a method to extract features which are able to discriminate between ictal and preictal states of a patient, in specific the numeric distance between q1 and q3 of the distribution of phase differences, We compare this values with other phase synchronization methods and test our hypothesis getting a p =  0.0001 with our proposed method.


Download data is not yet available.


Acharya UR, Sree SV, Swapna G, et al. Automated EEG analysis of epilepsy: A review. Knowl-Based Syst [Internet]. 2013;45:147-65. Available from: https://doi.org/10.1016/j.knosys.2013.02.014

Bruña R, Maestú F, Pereda E. Phase locking value revisited: teaching new tricks to an old dog. J Neural Eng [Internet]. 2018;15(5):056011. Available from: https://doi.org/10.1088/1741-2552/aacfe4

Fisher RS, Acevedo C, Arzimanoglou A, et al. ILAE Official Report: A practical clinical definition of epilepsy. Epilepsia [Internet]. 2014;55(4):475-82. Available from: https://doi.org/10.1111/epi.12550

Nair DR. Management of Drug-Resistant Epilepsy. Continuum (Minneap Minn) [Internet]. 2016;22(1):157-72. Available from: https://doi.org/10.1212/con.0000000000000297

Sejnowski TJ, Churchland PS, Movshon JA. Putting big data to good use in neuroscience. Nat Neurosci [Internet]. 2014;17:1440-1. Available from: https://doi.org/10.1038/nn.3839

van Mierlo P, Papadopoulou M, Carrette E, et al. Functional brain connectivity from EEG in epilepsy: Seizure prediction and epileptogenic focus localization. Prog Neurobiol [Internet]. 2014;121:19-35. Available from: https://doi.org/10.1016/j.pneurobio.2014.06.004

Camfield P, Camfield C. Idiopathic generalized epilepsy with generalized tonic-clonic seizures (IGE-GTC): a population-based cohort with >20 year follow up for medical and social outcome. Epilepsy Behav [Internet]. 2010;18(1-2):61-3. Available from: https://doi.org/10.1016/j.yebeh.2010.02.014

Ramgopal S, Thome-Souza S, Jackson M, et al. Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. Epilepsy Behav [Internet]. 2014;37:291-307. Available from: https://doi.org/10.1016/j.yebeh.2014.06.023

Chen H-H, Cherkassky V. Performance metrics for online seizure prediction. Neural Netw [Internet]. 2020;128:22-32. Available from: https://doi.org/10.1016/j.neunet.2020.04.022

Acharya UR, Hagiwara Y, Adeli H. Automated seizure prediction. Epilepsy Behav [Internet]. 2018;88:251-61. Available from: https://doi.org/10.1016/j.yebeh.2018.09.030

Kuhlmann L, Lehnertz K, Richardson MP, et al. Seizure prediction — ready for a new era. Nat Rev Neurol [Internet]. 2018;14(10):618-30. Available from: https://doi.org/10.1038/s41582-018-0055-2

Detti P, Vatti G, Zabalo Manrqiue de Lara G. EEG Synchronization Analysis for Seizure Prediction: A Study on Data of Noninvasive Recordings. Processes [Internet]. 2020;8(7):846. Available from: https://doi.org/10.3390/pr8070846

Espinoza-Valdez A, González-Garrido A, Luna B, et al. Epileptic brain reorganization dynamics on the basis of the probability of connections. NeuroReport [Internet]. 2015;27(1):1-5. Available from: https://doi.org/10.1097/WNR.0000000000000472

Mormann F, Andrzejak RG, Elger CE, Lehnertz K. Seizure prediction: the long and winding road. Brain [Internet]. 2007;130(2):314-33. Available from: https://doi.org/10.1093/brain/awl241

Lachaux J-P, Rodriguez E, Martinerie J, Varela FJ. Measuring phase synchrony in brain signals. Hum Brain Mapp [Internet]. 1999;8(4):194-208. Available from: https://doi.org/10.1002/(SICI)1097-0193(1999)8:4%3C194::AID-HBM4%3E3.0.CO;2-C

Klonowski W. Everything you wanted to ask about EEG but were afraid to get the right answer. Nonlinear Biomed Phys [Internet]. 2009;3(1):2. Available from: https://doi.org/10.1186/1753-4631-3-2

Fan M, Chou C-A. Detecting Abnormal Pattern of Epileptic Seizures via Temporal Synchronization of EEG Signals. IEEE Trans Biomed Eng [Internet]. 2019;66(3):601-8. Available from: https://doi.org/10.1109/TBME.2018.2850959

Yu H, Cai L, Wu X, et al. Investigation of phase synchronization of interictal EEG in right temporal lobe epilepsy. Physica A [Internet]. 2018;492:931-40. Available from: https://doi.org/10.1016/j.physa.2017.11.023

Alaei HS, Khalilzadeh MA, Gorji A. Online Epileptic Seizure Prediction Using Phase Synchronization and Two Time Characteristics: SOP and SPH. Int Clin Neurosci J [Internet]. 2019;7(1):16-25. Available from: https://doi.org/10.15171/icnj.2020.03

Stevenson N, Tapani K, Lauronen L, Vanhatalo S. A dataset of neonatal EEG recordings with seizure annotations. Sci Data [Internet]. 2019;6:190039. Available from: https://doi.org/10.1038/sdata.2019.39

Alducin Castillo J, Yanez Suárez O, Brust Carmona H. Electroencephalographic analysis of the functional conectivity in habituation by graphics theory. RMIB [Internet]. 2016;37(3):181-200. Available from: https://doi.org/10.17488/RMIB.37.3.3

Stam CJ, Nolte G, Daffertshofer A. Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum Brain Mapp [Internet]. 2007;28(11):1178-93. Available from: https://doi.org/10.1002/hbm.20346

Baselice F, Sorriso A, Rucco R, Sorrentino P. Phase Linearity Measurement: A Novel Index for Brain Functional Connectivity. IEEE Trans Med Imag [Internet]. 2019;38(4):873-82. Available from: https://doi.org/10.1109/TMI.2018.2873423

Shannon CE. 1 Shannon's Measure of Information. In: Aczél J, Daróczy Z (eds). Mathematics in Science and Engineering [Internet]. New York: Academic Press; Elsevier; 1975. 26-49p. Available from: https://doi.org/10.1016/S0076-5392(08)62730-7

Michel CM, Koenig T. EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review. NeuroImage [Internet]. 2018;180:577-93. Available from: https://doi.org/10.1016/j.neuroimage.2017.11.062

Wang L, Long X, Aarts R, et al. A broadband method of quantifying phase synchronization for discriminating seizure EEG signals. Biomed Signal Process Control [Internet]. 2018;52:371-83. Available from: https://doi.org/10.1016/j.bspc.2018.10.019

Sorrentino P, Ambrosanio M, Rucco R, Baselice F. An extension of Phase Linearity Measurement for revealing cross frequency coupling among brain areas. J Neuroeng Rehabil [Internet]. 2019;16(1):135. Available from: https://doi.org/10.1186/s12984-019-0615-8




How to Cite

Quirarte Tejeda , J. A., Flores Nuñez, J. L., & Romo Vázquez, R. (2021). Feature Extraction from Distributions of Phase Synchronization Values of EEG Recordings. Mexican Journal of Biomedical Engineering, 42(2), 78–89. Retrieved from https://rmib.mx/index.php/rmib/article/view/1140



Research Articles

Share on:

Dimensions Citation