Skip to main content

Advertisement

Log in

Mutual information in natural position order of electroencephalogram is significantly increased at seizure onset

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Epilepsy affects an estimated 60 million people worldwide. As many as 50% of people with epilepsy will continue to have seizures despite therapeutic dosages of appropriately selected antiepileptic drugs. Among proposed treatment modalities for persons with medication refractory epilepsy are implantable devices that rapidly detect and abort seizures. Computational resources in these devices are limited and much effort is directed to improving the efficiency of seizure detection. The goal of this study is to determine if electroencephalogram (EEG) may be reduced by the method of natural position order in a way that increases computation speed and reduces system memory requirements while preserving features relevant to detecting seizure onset. In this study we show increased mutual information (MI) at seizure onset in simultaneous channels of EEG reduced by natural position order with a 40-fold reduction in computation time and a fivefold reduction in system memory requirements. The trade-offs to EEG reduction by natural position order include decreased bandwidth and increased noise sensitivity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Anderson W, Kossoff E, Bergey G, Jallo G (2008) Implantation of a responsive neurostimulator device in patients with refractory epilepsy. Neurosurg Focus 25:E12

    Article  Google Scholar 

  2. Andrzejak R, Widman G, Lehnertz K, Rieke C, David P (2001) The epileptic process as nonlinear deterministic dynamics in a stochastic environment: an evaluation on mesial temporal lobe epilepsy. Epilepsy Res 44:129–140

    Article  CAS  PubMed  Google Scholar 

  3. Annegers J, Rocca W, Hauser W (1996) Causes of epilepsy: contributions of the Rochester epidemiology project. Mayo Clin Proc 71:570–575

    Article  CAS  PubMed  Google Scholar 

  4. Chechik G, Anderson A, Bar-Yosef O, Young E, Naftali T, Nelken I (2006) Reduction of information redundancy in the ascending auditory pathway. Neuron 51:359–368 (http://www.neuron.org/cgi/content/full/51/3/359/DC1/)

    Google Scholar 

  5. Das A, Das P, Roy A (2002) Applicability of Lyapunov exponent in EEG data analysis. Complex Int 9 Paper ID:das01. http://www.complexity.org.au/vol09/das01/

  6. Daw C, Halow J (1993) Evaluation and control of fluidization quality through chaotic time series analysis of pressure-drop measurements. AIChE Symp Ser 89:103–122

    Google Scholar 

  7. Escudero J, Hornero R, Abásolo D (2009) Interpretation of the auto-mutual information rate of decrease in the context of biomedical signal analysis. Application to electroencephalogram recordings. Physiol Meas 30:187–199

    Article  PubMed  Google Scholar 

  8. Ferrario M, Signorini M, Magenes G, Cerutti S (2006) Comparison of entropy-based regularity estimators: application to the fetal heart rate signal for the identification of fetal distress. IEEE Trans Bio-Med Eng 53:119–125

    Article  Google Scholar 

  9. Gedeon T, Holzer M, Pernarowski M (2003) Attractor reconstruction from interspike intervals is incomplete. Phys D 178:149–172

    Article  Google Scholar 

  10. Henon M (1976) A two-dimensional mapping with a strange attractor. Commun Math Phys 50:69–77

    Article  Google Scholar 

  11. Kahana M, Seelig D, Madsen J (2001) Theta returns. Curr Opin Neurobiol 11:739–744

    Article  CAS  PubMed  Google Scholar 

  12. Karamavruc A, Nigel N, Halow J (1995) Application of mutual information theory to fluid bed temperature and differential pressure signal analysis. Powder Technol 84:247–257

    Article  CAS  Google Scholar 

  13. Liu Z (2004) Measuring the degree of synchronization from time series data. Europhys Lett 68:19–25

    Article  CAS  Google Scholar 

  14. Liu Z, Hu B, Iasemidis L (2005) Detection of phase locking from non-stationary time series. Europhys Lett 71:200–206

    Article  CAS  Google Scholar 

  15. Lopes da Silva F (2005) EEG analysis: theory and practice. In: Niedermeyer E, Lopes Da Silva F (eds) Electroencephalography: basic principles, clinical applications, and related fields, 5th edn. Lippincott Williams & Wilkins, Philadelphia, pp 1199–1232

    Google Scholar 

  16. Nelken I, Cechik G, King T, Schnupp J (2005) Encoding stimulus information by spike numbers and mean response time in primary auditory cortex. J Comp Neurol 19:199–221

    Article  Google Scholar 

  17. Nyquist H (1928) Certain topics in telegraph transmission theory. Trans AIEE 47:617–644 (reprint as classic paper in Proc IEEE 90:280–305)

    Google Scholar 

  18. Panzeri S, Treves A (1996) Analytical estimates of limited sampling biases in different information measures. Netw Comput Neural Syst 7:87–107

    Article  Google Scholar 

  19. Pincus S (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA 88:2297–2301

    Article  CAS  PubMed  Google Scholar 

  20. Rieke C, Mormann F, Andrzejak R, Kreuz T, David P, Elger C, Lehnertz K (2003) Discerning nonstationarity from nonlinearity in seizure-free and preseizure EEG recordings from epilepsy patients. IEEE Trans Bio-Med Eng 50:634–639

    Article  Google Scholar 

  21. Shannon C (1949) Communication in the presence of noise. Proc IRE 37:10–21 (reprint as classic paper in Proc IEEE 86:447–457)

    Google Scholar 

  22. Shannon C, Weaver W (1949). The mathematical theory of communication. University of Illinois Press, Urbana

  23. Shorvon S (1996) The epidemiology and treatment of chronic and refractory epilepsy. Epilepsia 37:S1–S3

    Article  PubMed  Google Scholar 

  24. Stam C (2005) Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clin Neurophys 116:2266–2301

    Article  CAS  Google Scholar 

  25. Stein A, Eder H, Blum D, Drachev A, Fisher R (2000) An automated drug delivery system for focal epilepsy. Epilepsy Res 39:103–114

    Article  CAS  PubMed  Google Scholar 

  26. Strong S, Koberle, de Ruyter van Steveninck R, Bialek W (1998) Entropy and information in neural spike trains. Phys Rev Lett 80:197–200

  27. Treves A, Panzeri S (1995) The upward bias in measures of information derived from limited data samples. Neural Comput 7:399–407

    Article  Google Scholar 

  28. Worrell G, Parish L, Cranstoun S, Jonas R, Baltuch G, Litt B (2004) High-frequency oscillations and seizure generation in neocortical epilepsy. Brain 127:1496–1506

    Article  PubMed  Google Scholar 

  29. Yu Z, Mao G, Zhou L, Anh V (2007) A mutual information based sequence distance for vertebrate phylogeny using complete mitochondrial genomes. Paper presented at Third International Conference on Natural Computation (ICNC 2007), pp 253–257. http://eprints.qut.edu.au/14327/1/14327.pdf

  30. Zoldi S, Krystal A, Greenside H (2000) Stationarity and redundancy of multichannel EEG data recorded during generalized tonic-clonic seizures. Brain Topogr 12:187–200

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Charles W. Hall Jr..

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hall, C.W., Sarkar, A. Mutual information in natural position order of electroencephalogram is significantly increased at seizure onset. Med Biol Eng Comput 49, 133–141 (2011). https://doi.org/10.1007/s11517-010-0684-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-010-0684-0

Keywords

Navigation