Advertisement

Mean amplitude spectrum based epileptic state classification for seizure prediction using convolutional neural networks

  • Wenbin Hu
  • Jiuwen CaoEmail author
  • Xiaoping Lai
  • Junbiao Liu
Original Research
First Online:
  • 18 Downloads

Abstract

An increasing number of algorithms have been proposed for epileptic seizure prediction in recent years. But most of them are based on a partition of the electroencephalograph (EEG) signal of an epileptic patient into preictal, ictal (seizure), and interictal states. In this paper, we propose to further divide the preictal interval into multiple subintervals. Besides discriminating the seizure state from the preictal and interictal states, we also distinguish the preictal subintervals from each other. An epileptic state classification algorithm for epileptic EEG signals is then developed. The amplitude spectrums of EEG signals from 18 channels are firstly calculated and divided into 19 frequency subbands. The mean amplitude spectrum (MAS) on each of the 19 frequency subbands is then computed for each channel to form a MAS map of size 18\(\times\)19. Finally, the MAS map is fed to a convolutional neural network (CNN) for feature extraction and a support vector machine (SVM) is employed for the epileptic state classification. Experiments show that, for a three-subinterval partition of the preictal state, a classification accuracy of 86.25% has been achieved on the CHB-MIT database by the MAS-based epileptic state classification algorithm using CNN and SVM.

Keywords

Seizure prediction Preictal state CNN Mean amplitude spectrum Support vector machine EEG 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

This work was supported by the National Nature Science Foundation of China under Grant 61503104, and supported in part by the K. C. Wong Education Foundation and DAAD, the Zhejiang basic public welfare research program LGF18F010007, and the special fund project of information development in Shanghai: XX-XXFZ-02-18-2862.

Compliance with ethical standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Acharya UR, Oh SL, Hagiwara Y (2017) Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med 100:270–280CrossRefGoogle Scholar
  2. Achilles F, Tombari F, Belagiannis V (2018) Convolutional neural networks for real-time epileptic seizure detection. Compu MethodsBiomech Biomed Eng Imaging Vis 6:264–269CrossRefGoogle Scholar
  3. Alarcon G, Binnie CD, Elwes RDC (1995) Power spectrum and intracranial EEG patterns at seizure onset in partial epilepsy. Electroencephalogr Clin Neurophysiol 94:326–337CrossRefGoogle Scholar
  4. Blanco S, Garay A, Coulombie D (2013) Comparison of frequency bands using spectral entropy for epileptic seizure prediction. ISRN Neurol 2013:287327.  https://doi.org/10.1155/2013/287327 CrossRefGoogle Scholar
  5. Cao J, Zhang K, Yong H, Lai X, Chen B, Lin Z (2018) Extreme learning machine with affine transformation inputs in an activation function. IEEE Trans Neural Netw Lear Syst 99:1–15.  https://doi.org/10.1109/TNNLS.2018.2877468 Google Scholar
  6. Celka P, Colditz P (2002) A computer-aided detection of EEG seizures in infants: a singular-spectrum approach and performance comparison. IEEE Trans Biomed Eng 49:455–462CrossRefGoogle Scholar
  7. Elger CE (2001) Future trends in epileptology. Curr Opin Neurol 14:185–186CrossRefGoogle Scholar
  8. Iasemidis LD, Sackellares JC (1996) Chaos theory and epilepsy. The Neuroscientist 2:118–126CrossRefGoogle Scholar
  9. Le Van Quyen M, Martinerie J, Navarro V (2001) Anticipation of epileptic seizures from standard EEG recordings. The Lancet 357:183–188CrossRefGoogle Scholar
  10. Litt B, Esteller R, Echauz J (2001) Seizure precursors may begin hours in advance of temporal lobe seizures: a report of five patients. Neuron 29:51–64CrossRefGoogle Scholar
  11. Martinerie J, Adam C, Le Van Quyen M (1998) Epileptic seizures can be anticipated by non-linear analysis. Nat Med 4(10):1173CrossRefGoogle Scholar
  12. Mirowski PW, LeCun Y, Madhavan D (2008) Comparing SVM and convolutional networks for epileptic seizure prediction from intracranial EEG. In: Machine learning for signal processing, pp 244-249Google Scholar
  13. Mormann F, Kreuz T, Rieke C (2005) On the predictability of epileptic seizures. Clin Neurophysiol 116:569–587CrossRefGoogle Scholar
  14. Mormann F, Andrzejak RG, Elger CE (2006) Seizure prediction: the long and winding road. Brain 130:314–333CrossRefGoogle Scholar
  15. Netoff T, Park Y, Parhi K (2009) Seizure prediction using cost-sensitive support vector machine. In: Engineering in medicine and biology society, pp 3322-3325Google Scholar
  16. Nigam VP, Graupe D (2004) A neural-network-based detection of epilepsy. Neurol Res 26:55–60CrossRefGoogle Scholar
  17. Park Y, Luo L, Parhi KK (2011) Seizure prediction with spectral power of EEG using cost-sensitive support vector machines. Epilepsia 52:1761–1770CrossRefGoogle Scholar
  18. Perucca P, Dubeau F, Gotman J (2013) Intracranial electroencephalographic seizure-onset patterns: effect of underlying pathology. Brain 137:183–196CrossRefGoogle Scholar
  19. Skjei KL, Dlugos DJ (2011) The evaluation of treatment-resistant epilepsy. Semin Pediatr Neurol 18:150–170CrossRefGoogle Scholar
  20. Song J, Zhang R (2017) Application of extreme learning machine to epileptic seizure detection based on lagged Poinca\(\acute{r}\)e plots. Multidimens Syst Signal Process 28(3):945–959CrossRefGoogle Scholar
  21. Srinivasan V, Eswaran C, Sriraam N (2005) Artificial neural network based epileptic detection using time-domain and frequency-domain features. J Med Syst 29:647–660CrossRefGoogle Scholar
  22. Srinivasan V, Eswaran C, Sriraam N (2007) Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans inf Technol Biomed 11:288–295CrossRefGoogle Scholar
  23. Truong ND, Nguyen AD, Kuhlmann L (2017) A generalised seizure prediction with convolutional neural networks for intracranial and scalp electroencephalogram data analysis. arXiv preprint arXiv:1707.01976v2
  24. Wilden JA, Cohen-Gadol AA (2012) Evaluation of first nonfebrile seizures. Am Family Phys 86(4)Google Scholar
  25. Williamson JR, Bliss DW, Browne DW (2011) Epileptic seizure prediction using the spatiotemporal correlation structure of intracranial EEG. In: Acoustics, speech and signal processing (ICASSP), pp 665-668Google Scholar
  26. Witte H, Iasemidis LD, Litt B (2003) Special issue on epileptic seizure prediction. IEEE Trans Biomed Eng 50:537–539CrossRefGoogle Scholar
  27. Zhang Z, Parhi KK (2016) Low-complexity seizure prediction from iEEG/sEEG using spectral power and ratios of spectral power. IEEE Trans Biomed Circ Syst 10:693–706CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Wenbin Hu
    • 1
  • Jiuwen Cao
    • 1
    • 2
    Email author
  • Xiaoping Lai
    • 1
  • Junbiao Liu
    • 2
  1. 1.Institute of Information and ControlHangzhou Dianzi UniversityHangzhouChina
  2. 2.Hangzhou Neuro Science and Technology Co. LtdHangzhouChina

Personalised recommendations

Cite article

Buy options