Biomedical Engineering Letters

, Volume 3, Issue 2, pp 80–86 | Cite as

Nonstationary-epileptic-spike detection algorithm in EEG signal using SNEO

Original Article



This correspondence presents the evaluation of nonstationary epileptic spike (ES) detection algorithm in the electroencephalogram (EEG) signal using the smoothed nonlinear energy operator (SNEO) based on the different time-domain window functions. However, the incorporation of adaptive threshold determination procedure enhances the performance of proposed ES detector.


The detection procedure exploits the fact that the presence of instantaneous ES corresponds to the high instantaneous energy content at the high frequencies. In addition to the stochastic amplitude, sign and the location of appearance of triangular spikes in the synthetic EEG signal, its base-width is also considered to be variable for the nonstationary analysis. The five pairs of EEG signals, obtained from electrodes placed on the left and right frontal cortex of male adult WAG/Rij rats, are used for the testing of proposed adaptive scheme in the real-time environment, which is a genetic animal model of human epilepsy.


The simulation results are presented to demonstrate that the choice of window function plays a significant role in the efficient detection of ESs. The computational complexity is found to be in trade-off relationship with the detection accuracy of algorithm.


It may be inferred that the real-time EEG signals (rat data) can be processed and analyzed using the proposed adaptive scheme for the ES detection, which supersedes the conventional techniques.


EEG Epileptic spike Nonlinear energy operators (NEO) Nonstationarity Teager energy operator (TEO) 


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  1. [1]
    Bodenstein G, Praetorious HM. Feature extraction from the electroencephalogram by adaptive segmentation. Proc IEEE. 1977; 65(5):642–652.CrossRefGoogle Scholar
  2. [2]
    Guyton AC, Hall JE. Text book of medical physiology. 11th ed. China, Elsevier Saunders; 2006.Google Scholar
  3. [3]
    Bajaj V, Pachori RB. Epileptic seizure detection based on the instantaneous area of analytic intrinsic mode functions of EEG signals. Biomed Eng Lett. 2013; 3(1):17–21.CrossRefGoogle Scholar
  4. [4]
    Garg G, Singh V, Gupta JRP, Mittal AP. Wrapper based wavelet feature optimization for EEG signals. Springer, Biomed Eng Lett. 2012; 2(1):24–37.CrossRefGoogle Scholar
  5. [5]
    Kumar Y, Dewal ML, Anand RS. Relative wavelet energy and wavelet entropy based epileptic brain signals classification. Springer, Biomed Eng Lett. 2012; 2(3):147–157.CrossRefGoogle Scholar
  6. [6]
    Tzallas AT, Oikonomous VP, Fotiadis DI. Epileptic spike detection using a Kalman filter based approach. Conf Proc IEEE Eng Med Biol Soc. 2006; 501-3.Google Scholar
  7. [7]
    Exarchos TP, Tzallas AT, Fotiadis DI, Konitsiotis S, Giannopoulos S. EEG transient event detection and classification using association rules. IEEE T Inform Technol Biomed. 2006; 10(3):451–457.CrossRefGoogle Scholar
  8. [8]
    Penny WD, Roberts SJ. Dynamic models for nonstationary signal segmentation. Comput Biomed Res. 1999; 32(6):483–502.CrossRefGoogle Scholar
  9. [9]
    Kim KH, Kim SJ. Neural spike sorting under nearly 0-dB signal-to-noise ratio using nonlinear energy operator and artificial neural network classifier. IEEE T Bio-med Eng. 2000; 47(10):1406–1411.CrossRefGoogle Scholar
  10. [10]
    Maragos P, Kaiser JF, Quatieri TF. On amplitude and frequency demodulation using energy operators. IEEE T Signal Process. 1993; 41(4):1532–1550.MATHCrossRefGoogle Scholar
  11. [11]
    Potamianos A, Maragos P. A comparison of the energy operator and the Hilbert transform approach to signal and speech demodulation. Signal Process. 1994; 37(1):95–120.MATHCrossRefGoogle Scholar
  12. [12]
    Mukhopadhyay S, Ray GC. A new interpretation of nonlinear energy operator and its efficacy in spike detection. IEEE T Biomed Eng. 2000; 45(2):1406–1411.Google Scholar
  13. [13]
    Kaiser JF. On a simple algorithm to calculate the energy of a signal. Proc IEEE Int Conf Acoustic Speech Signal Process. 1990; 381-4.Google Scholar
  14. [14]
    Kaiser JF. On Teager’s algorithm and its generalization to continuous signals. Proc IEEE Digit Signal Process. 1990.Google Scholar
  15. [15]
    Proakis JG, Manolakis DG. Digital Signal Processing: Principles, Algorithms and Applications, 3rd ed. USA: Pearson Education; 1996.Google Scholar
  16. [16]
    Semmaoui H, Drolet J, Lakhssassi A, Sawan M. Setting adaptive spike detection threshold for smoothed TEO based on robust statistics theory. IEEE T Bio-med Eng. 2012; 59(2):474–482.CrossRefGoogle Scholar
  17. [17]
    Papoulis A. Probability Random Variables and Stochastic Processes, 3rd ed. McGraw-Hill; 1984.MATHGoogle Scholar
  18. [18]
    White AM, Williams PA, Ferraro DJ, Clark S, Kadam SD, Dudek FE, Staley KJ. Efficient unsupervised algorithms for the detection of seizures in continuous EEG recordings from rats after brain injury. J. Neurosci Meth. 2006; 152(1-2):255–266.CrossRefGoogle Scholar
  19. [19]
    Quiroga RQ, Kraskov A, Kreuz T, Grassberger P. Performance of different synchronization measures in real data: a case study on electroencephalographic signals. Phys Rev E. 2002; 65(041903): 1–14.Google Scholar
  20. [20]
    Quiroga RQ, Kreuz T, Grassberger P. Event synchronization: a simple and fast method to measure synchronicity and time delay patterns. Phys Rev E. 2002; 66(041904):1–9.Google Scholar
  21. [21]
    Shao C, Li S, Fan J. EEG spike detection based on qualitative modeling of visual observation. Proc Int Conf Fuzzy Syst Knowl Discov. 2007; 2:745–748.Google Scholar
  22. [22]
    Aydin S. Comparison of power spectrum predictors in computing coherence functions for intracortical EEG signals. Ann Biomed Eng. 2009; 37(1):192–200.CrossRefGoogle Scholar

Copyright information

© Korean Society of Medical and Biological Engineering and Springer 2013

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringThapar UniversityPatialaIndia

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