Skip to main content
SpringerLink
Log in

Multichannel interictal spike activity detection using time–frequency entropy measure

  • Scientific Paper
  • Published:
Australasian Physical & Engineering Sciences in Medicine Aims and scope Submit manuscript

Abstract

Localization of interictal spikes is an important clinical step in the pre-surgical assessment of pharmacoresistant epileptic patients. The manual selection of interictal spike periods is cumbersome and involves a considerable amount of analysis workload for the physician. The primary focus of this paper is to automate the detection of interictal spikes for clinical applications in epilepsy localization. The epilepsy localization procedure involves detection of spikes in a multichannel EEG epoch. Therefore, a multichannel Time–Frequency (T–F) entropy measure is proposed to extract features related to the interictal spike activity. Least squares support vector machine is used to train the proposed feature to classify the EEG epochs as either normal or interictal spike period. The proposed T–F entropy measure, when validated with epilepsy dataset of 15 patients, shows an interictal spike classification accuracy of 91.20%, sensitivity of 100% and specificity of 84.23%. Moreover, the area under the curve of Receiver Operating Characteristics plot of 0.9339 shows the superior classification performance of the proposed T–F entropy measure. The results of this paper show a good spike detection accuracy without any prior information about the spike morphology.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. WHO Media Center WHO | (2016) Epilepsy. World Health Organization

  2. Barkley GL, Baumgartner C (2003) MEG and EEG in epilepsy. J Clin Neurophysiol 20:163–178. doi:10.1097/00004691-200305000-00002

    Article  PubMed  Google Scholar 

  3. Brodbeck V, Spinelli L, Lascano AM, Wissmeier M, Vargas MI, Vulliemoz S et al (2011) Electroencephalographic source imaging: a prospective study of 152 operated epileptic patients. Brain 134:2887–2897. doi:10.1093/brain/awr243

    Article  PubMed  PubMed Central  Google Scholar 

  4. Park CJ, Seo JH, Kim D, Abibullaev B, Kwon H, Lee YH et al (2015) EEG source imaging in partial epilepsy in comparison with presurgical evaluation and magnetoencephalography. J Clin Neurol (Korea) 11:319–330. doi:10.3988/jcn.2015.11.4.319

    Article  Google Scholar 

  5. Lu Y, Worrell GA, Zhang HC, Yang L, Brinkmann B, Nelson C et al (2014) Noninvasive imaging of the high frequency brain activity in focal epilepsy patients. IEEE Trans Biomed Eng 61:1660–1667. doi:10.1109/TBME.2013.2297332

    Article  PubMed  PubMed Central  Google Scholar 

  6. Lee C, Kim JS, Jeong W, Chung CK (2014) Usefulness of interictal spike source localization in temporal lobe epilepsy: electrocorticographic study. Epilepsy Res 108:448–458. doi:10.1016/j.eplepsyres.2013.12.008

    Article  PubMed  Google Scholar 

  7. Wang G, Worrell G, Yang L, Wilke C, He B (2011) Interictal spike analysis of high-density eeg in patients with partial epilepsy. Clin Neurophysiol 122:1098–1105. doi:10.1016/j.clinph.2010.10.043

    Article  PubMed  Google Scholar 

  8. Zwoliński P, Roszkowski M, Zygierewicz J, Haufe S, Nolte G, Durka PJ (2010) Open database of epileptic EEG with MRI and postoperational assessment of foci–a real world verification for the EEG inverse solutions. Neuroinformatics 8:285–299. doi:10.1007/s12021-010-9086-6

    Article  PubMed  PubMed Central  Google Scholar 

  9. Scherg M, Ille N, Weckesser D, Ebert A, Ostendorf A, Boppel T et al (2012) Fast evaluation of interictal spikes in long-term EEG by hyper-clustering. Epilepsia 53:1196–1204. doi:10.1111/j.1528-1167.2012.03503.x

    Article  PubMed  Google Scholar 

  10. Acharya UR, Fujita H, Sudarshan VK, Bhat S, Koh JEW (2015) Application of entropies for automated diagnosis of epilepsy using EEG signals: a review. Knowl-Based Syst 88:85–96. doi:10.1016/j.knosys.2015.08.004

    Article  Google Scholar 

  11. Shen CP, Liu ST, Zhou WZ, Lin FS, Lam AYY, Sung HY et al (2013) A physiology-based seizure detection system for multichannel EEG. PLoS ONE. doi:10.1371/journal.pone.0065862

  12. Xiang J, Li C, Li H, Cao R, Wang B, Han X et al (2015) The detection of epileptic seizure signals based on fuzzy entropy. J Neurosci Methods 243:18–25. doi:10.1016/j.jneumeth.2015.01.015

    Article  PubMed  Google Scholar 

  13. Zhanfeng Ji, Sugi T, Goto S, Xingyu Wang, Ikeda A, Nagamine T et al (2011) An Automatic spike detection system based on elimination of false positives using the large-area context in the scalp EEG. IEEE Trans Biomed Eng 58:2478–2488. doi:10.1109/TBME.2011.2157917

    Article  Google Scholar 

  14. Faust O, Acharya UR, Adeli H, Adeli A (2015) Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. Seizure 26:56–64. doi:10.1016/j.seizure.2015.01.012

    Article  PubMed  Google Scholar 

  15. Bhardwaj S, Jadhav P, Adapa B, Acharyya A, Naik GR (2015) Online and automated reliable system design to remove blink and muscle artefact in EEG. 37th Annual international conference of the IEEE engineering in medicine and biology society (EMBC), IEEE; 2015, pp. 6784–6787. doi:10.1109/EMBC.2015.7319951

  16. Jadhav PN, Shanamugan D, Chourasia A, Ghole AR, Acharyya A, Naik G (2014) Automated detection and correction of eye blink and muscular artefacts in EEG signal for analysis of Autism Spectrum Disorder. 36th Annual international conference of the IEEE engineering in medicine and biology society, vol. 2014, IEEE; 2014, pp. 1881–1884. doi:10.1109/EMBC.2014.6943977

  17. Feis RA, Smith SM, Filippini N, Douaud G, Dopper EGP, Heise V et al (2015) ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI. Front Neurosci 9:395. doi:10.3389/fnins.2015.00395

    Article  PubMed  PubMed Central  Google Scholar 

  18. Ayoubian L, Lacoma H, Gotman J (2013) Automatic seizure detection in SEEG using high frequency activities in wavelet domain. Med Eng Phys 35:319–328. doi:10.1016/j.medengphy.2012.05.005

    Article  CAS  PubMed  Google Scholar 

  19. Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM (2011) Brainstorm: a user-friendly application for MEG/EEG analysis. Comput Intell Neurosci. doi:10.1155/2011/879716

  20. Deng Y, Bao F, Deng X, Wang R, Kong Y, Dai Q (2016) Deep and structured robust information theoretic learning for image analysis. IEEE Transact Image Process. doi:10.1109/TIP.2016.2588330

  21. Kong Y, Deng Y, Dai Q (2015) Discriminative clustering and feature selection for brain MRI segmentation. IEEE Signal Process Lett 22:573–577. doi:10.1109/LSP.2014.2364612

    Article  Google Scholar 

  22. Acharya UR, Molinari F, Sree SV, Chattopadhyay S, Ng KH, Suri JS (2012) Automated diagnosis of epileptic EEG using entropies. Biomed Signal Process Control 7:401–408. doi:10.1016/j.bspc.2011.07.007

    Article  Google Scholar 

  23. Zhang Y, Liu B, Ji X, Huang D (2016) Classification of EEG signals based on autoregressive model and wavelet packet decomposition. Neural Processing Lett. doi:10.1007/s11063-016-9530-1

    Google Scholar 

  24. Chai R, Naik G, Nguyen TN, Ling S, Tran Y, Craig A, et al (2016) Driver Fatigue classification with independent component by entropy rate bound minimization analysis in an EEG-based System. IEEE J Biomed Health Inform. doi:10.1109/JBHI.2016.2532354.

    PubMed  Google Scholar 

  25. Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA 88:2297–2301. doi:10.1073/pnas.88.6.2297

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Bajaj V, Pachori R (2011) Application of the sample entropy for discrimination between seizure and seizure-free EEG signals. Proceedings of the fifth Indian international conference on artificial intelligence application :1232–1237

  27. Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278:H2039–H2049. doi:10.1103/physreva.29.975

    CAS  PubMed  Google Scholar 

  28. Chen W, Wang Z, Xie H, Yu W (2007) Characterization of surface EMG signal based on fuzzy entropy. IEEE Trans Neural Syst Rehabil Eng 15:266–272. doi:10.1109/TNSRE.2007.897025

    Article  PubMed  Google Scholar 

  29. Rodin E, Constantino T, Rampp S, Wong PK (2009) Spikes and epilepsy. Clin EEG Neurosci 40:288–299. doi:10.1177/155005940904000411.

    Article  CAS  PubMed  Google Scholar 

  30. Pohjalainen J, Räsänen O, Kadioglu S (2015) Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits. Comput Speech Lang 29:145–171. doi:10.1016/j.csl.2013.11.004

    Article  Google Scholar 

  31. Chai R, Tran Y, Naik GR, Nguyen TN, Ling SH, Craig A et al (2016) Classification of EEG based-mental fatigue using principal component analysis and Bayesian neural network. 38th Annual international conference of the IEEE engineering in medicine and biology society (EMBC), IEEE; 2016 pp. 4654–4657. doi:10.1109/EMBC.2016.7591765

  32. Shin Y, Lee S, Ahn M, Cho H, Jun SC, Lee H-N. (2015) Noise robustness analysis of sparse representation based classification method for non-stationary EEG signal classification. Biomed Signal Process Control 21:8–18. doi:10.1016/j.bspc.2015.05.007

    Article  Google Scholar 

  33. Chua KC, Chandran V, Acharya UR, Lim CM (2011) Application of higher order spectra to identify epileptic EEG. J Med Syst 35:1563–1571. doi:10.1007/s10916-010-9433-z

    Article  PubMed  Google Scholar 

  34. Lu N, Li T, Pan J, Ren X, Feng Z, Miao H (2015) Structure constrained semi-nonnegative matrix factorization for EEG-based motor imagery classification. Comput Biol Med 60:32–39. doi:10.1016/j.compbiomed.2015.02.010

    Article  PubMed  Google Scholar 

  35. Sharma R, Pachori RB, Acharya UR (2015) Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals. Entropy 17:669–691. doi:10.3390/e17020669

    Article  Google Scholar 

  36. Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9:293–300. doi:10.1023/A:1018628609742

    Article  Google Scholar 

  37. Acharya UR, Vinitha Sree S, Swapna G, Martis RJ, Suri JS (2013) Automated EEG analysis of epilepsy: a review. Knowl-Based Syst 45:147–165. doi:10.1016/j.knosys.2013.02.014

    Article  Google Scholar 

  38. İnan ZH, Kuntalp M (2007) A study on fuzzy C-means clustering-based systems in automatic spike detection. Comput Biol Med 37:1160–1166. doi:10.1016/j.compbiomed.2006.10.010

    Article  PubMed  Google Scholar 

  39. Chavakula V, Sánchez Fernández I, Peters JM, Popli G, Bosl W, Rakhade S et al (2013) Automated quantification of spikes. Epilepsy Behav 26:143–152. doi:10.1016/j.yebeh.2012.11.048

    Article  PubMed  Google Scholar 

  40. Seker Yilmaz B, Okuyaz C, Komur M (2013) Predictors of intractable childhood epilepsy. Pediatr Neurol 48:52–55. doi:10.1016/j.pediatrneurol.2012.09.008

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to express sincere thanks to the reviewers for providing appropriate suggestions to improve the quality of the manuscript.

Author information

Authors and Affiliations

  1. Department of Electronics and Instrumentation Engineering, St. Joseph’s College of Engineering, Anna University, OMR, Chennai, 600119, India

    Palani Thanaraj

  2. Department of Computer Science and Engineering, St. Joseph’s College of Engineering, Anna University, OMR, Chennai, 600119, India

    B. Parvathavarthini

Authors
  1. Palani Thanaraj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. Parvathavarthini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Palani Thanaraj.

Ethics declarations

Conflict of interest

None Declared.

Ethical approval

This study used publicly available epilepsy database obtained from http://eeg.pl/epi. It is available to researchers guided by the norms of ‘Information sharing agreement’ described in the works of Piotr Zwoliński et al.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Thanaraj, P., Parvathavarthini, B. Multichannel interictal spike activity detection using time–frequency entropy measure. Australas Phys Eng Sci Med 40, 413–425 (2017). https://doi.org/10.1007/s13246-017-0550-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13246-017-0550-6

Keywords

Search

Navigation