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Seizure onset detection based on frequency domain metric of empirical mode decomposition

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Abstract

This paper explores the data-driven properties of the empirical mode decomposition (EMD) for detection of epileptic seizures. A new method in frequency domain is presented to analyze intrinsic mode functions (IMFs) decomposed by EMD. They are used to determine whether the electroencephalogram (EEG) recordings contain seizure or not. Energy levels of the IMFs are extracted as threshold level to detect the changes caused by seizure activity. A scalar value energy resulting from the energy levels is individually used as an indicator of the epileptic EEG without the requirements of multidimensional feature vector and complex machine learning algorithms. The proposed methods are tested on different EEG recordings to evaluate the effectiveness of the proposed method and yield accuracy rate up to 97.89%.

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References

  1. Adeli, H., Zhou, Z., Dadmehr, N.: Analysis of EEG records in an epileptic patient using wavelet transform. J. Neurosci. Methods 123, 69–87 (2003). https://doi.org/10.1016/S0165-0270(02)00340-0

    Article  Google Scholar 

  2. Alam, S.S., Bhuiyan, M.: Detection of seizure and epilepsy using higher order statistics in the EMD domain. IEEE J. Biomed. Health Inf. 17, 312–318 (2013). https://doi.org/10.1109/JBHI.2012.2237409

    Article  Google Scholar 

  3. Alkishriwo, O.A., Akan, A., Chaparro, L.F.: Intrinsic mode chirp decomposition of non-stationary signals. IET Signal Proc. 8(3), 267–276 (2014). https://doi.org/10.1049/iet-spr.2013.0396

    Article  Google Scholar 

  4. Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P., Elger, C.E.: Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 64, 061907 (2001). https://doi.org/10.1103/PhysRevE.64.061907

    Article  Google Scholar 

  5. Aydin, S., Saraouglu, H.M., Kara, S.: Log energy entropy-based EEG classification with multilayer neural networks in seizure. Ann. Biomed. Eng. 37, 2626–2630 (2009). https://doi.org/10.1007/s10439-009-9795-x

    Article  Google Scholar 

  6. Bagheri, A., Adorno, D.P., Rizzo, P., Barraco, R., Bellomonte, L.: Empirical mode decomposition and neural network for the classification of electroretinographic data. Med. Biol. Eng. Comput. 52(7), 619–628 (2014)

    Article  Google Scholar 

  7. Bajaj, V., Pachori, R.B.: Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans. Inf. Technol. Biomed. (2012). https://doi.org/10.1109/TITB.2011.2181403

    Google Scholar 

  8. Bhardwaj, S., Jadhav, P., Adapa, B., Acharyya, A., Naik, G.R.: Online and automated reliable system design to remove blink and muscle artefact in EEG. In: Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, pp. 6784–6787 (2015)

  9. Das, A.B., Bhuiyan, M.I.H., Alam, S.S.: Classification of eeg signals using normal inverse gaussian parameters in the dual-tree complex wavelet transform domain for seizure detection. SIViP 10(2), 259–266 (2016)

    Article  Google Scholar 

  10. Flandrin, P., Goncalves, P.: Emprical mode decompositions as data-driven wavelet-like expansions. Int. J. Wavel. Multiresolut. Inf. Process. 02, 477–496 (2004). https://doi.org/10.1142/S0219691304000561

    Article  MATH  Google Scholar 

  11. Flandrin, P., Rilling, G., Gonc, P.: Empirical mode decomposition as a filter bank. IEEE Signal Process. Lett. 11, 112–114 (2004)

    Article  Google Scholar 

  12. Guler, N.F., Ubeyli, E.D., Guler, I.: Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Syst. Appl. 29, 506–514 (2005). https://doi.org/10.1016/j.eswa.2005.04.011

    Article  Google Scholar 

  13. Guo, Y., Huang, S., Li, Y., Naik, G.R.: Edge effect elimination in single-mixture blind source separation. Circuits Syst. Signal Process. 32(5), 2317–2334 (2013a). https://doi.org/10.1007/s00034-013-9556-9

    Article  MathSciNet  Google Scholar 

  14. Guo, Y., Naik, G.R., Nguyen, H.: Single channel blind source separation based local mean decomposition for biomedical applications. In: Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pp. 6812–6815 (2013)

  15. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. A Math. Phys. Eng. Sci. 454, 903–995 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  16. Jadhav, P., Shanamugan, D., Chourasia, A., Ghole, A., Acharyya, A., Naik, G.: Automated detection and correction of eye blink and muscular artefacts in EEG signal for analysis of autism spectrum disorder. In: Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, pp. 1881–1884 (2014)

  17. Kiymikk, M.K., Guler, I., Dizibuyuk, A., Akin, M.: Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application. Comput. Biol. Med. 35, 603–616 (2005)

    Article  Google Scholar 

  18. Liang, S.F., Wang, H.C., Chang, W.L.: Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection. EURASIP J. Adv. Signal Process. Article ID 853434, p. 15 (2010)

  19. Mert, A., Akan, A.: Detrended fluctuation thresholding for empirical mode decomposition based denoising. Digit. Signal Proc. 32, 48–56 (2014). https://doi.org/10.1016/j.dsp.2014.06.006

    Article  Google Scholar 

  20. Mihandoost, S., Mazlaghani, M., Amirani, M., Mihandoost, A.: Automatic feature extraction using generalised autoregressive conditional heteroscedasticity model: an application to electroencephalogram classification. IET Signal Proc. 6(9), 829–838 (2012). https://doi.org/10.1049/iet-spr.2011.0338

    Article  Google Scholar 

  21. Minasyan, G.R., Chatten, J.B., Chatten, M.J., Harner, R.N.: Patient-specific early seizure detection from scalp electroencephalogram. J. Clin. Neurophysiol. 27, 163–178 (2010). https://doi.org/10.1097/WNP.0b013e3181e0a9b6

    Article  Google Scholar 

  22. Naik, G.R., Selvan, S.E., Nguyen, H.T.: Single-channel emg classification with ensemble-empirical-mode-decomposition-based ica for diagnosing neuromuscular disorders. IEEE Trans. Neural Syst. Rehabil. Eng. 24(7), 734–743 (2016)

    Article  Google Scholar 

  23. Nasehi, S., Pourghassem, H.: Patient-specific epileptic seizure onset detection algorithm based on spectral features and ipsonn classifier. In: Communication Systems and Network Technologies (CSNT), 2013 International Conference on IEEE, pp. 186–190 (2013)

  24. Nesaei, S., Sharafat, A.R.: Real-time mining of epileptic seizure precursors via nonlinear mapping and dissimilarity features. IET Signal Proc. 9(3), 193–200 (2015)

    Article  Google Scholar 

  25. Orhan, U., Hekim, M., Ozer, M.: Eeg signals classification using the \(k\)-means clustering and a multilayer perceptron neural network model. Expert Syst. Appl. 38(10), 13475–13481 (2011)

    Article  Google Scholar 

  26. Orosco, L., Correa, A.G., Leber, E.L.: Epileptic seizures detection based on empirical mode decomposition of eeg signals. In: Management of Epilepsy-Research, Results and Treatment. InTech (2011)

  27. Orosco, L., Correa, A.G., Laciar, E.: Review: a survey of performance and techniques for automatic epilepsy detection. J. Med. Biol. Eng. 33(6), 526–537 (2013)

    Article  Google Scholar 

  28. Özbeyaz,A., Arica, S.: Familiar/unfamiliar face classification from EEG signals by utilizing pairwise distant channels and distinctive time interval. Signal Image Video Process. 1–8 (2018). https://doi.org/10.1007/s11760-018-1269-x

  29. Pachori, R.B., Bajaj, V.: Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition. Comput. Methods Progr. Biomed. 104, 373–381 (2011). https://doi.org/10.1016/j.cmpb.2011.03.009

    Article  Google Scholar 

  30. Polat, K., Gunes, S.: Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl. Math. Comput. 187, 1017–1026 (2007). https://doi.org/10.1016/j.amc.2006.09.022

    MathSciNet  MATH  Google Scholar 

  31. Rato, R., Ortigueira, M., Batista, A.: On the hht, its problems, and some solutions. Mech. Syst. Signal Process. 22, 1374–1394 (2008)

    Article  Google Scholar 

  32. Rilling, G., Flandrin, P.: One or two frequencies? The empirical mode decomposition answers. IEEE Trans. Signal Process. 56, 85–95 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  33. Shoeb, A., Edwards, H., Connolly, J., Bourgeois, B., Ted Treves, S., Guttag, J.: Patient-specific seizure onset detection. Epilepsy Behav. 5, 483–498 (2004). https://doi.org/10.1016/j.yebeh.2004.05.005

    Article  Google Scholar 

  34. Shoeb, A.H.: Appliction of machine learning to epileptic seizure onset detection and treatment. Ph.D., Massachusetts Institute Technology (2009)

  35. Tibdewal, M.N., Fate, R.R., Mahadevappa, M., Ray, A.K., Malokar, M.: Classification of artifactual EEG signal and detection of multiple eye movement artifact zones using novel time-amplitude algorithm. SIViP 11(2), 333–340 (2017)

    Article  Google Scholar 

  36. Tzallas, A.T., Tsalikakis, D.G., Karvounis, E.C., Astrakas, L., Tzaphlidou, M., Tsipouras, M.G., Konitsiotis, S.: Automated Epileptic Seizure Detection Methods: A Review Study. INTECH Open Access Publisher, New York (2012)

    Google Scholar 

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Acknowledgements

This study was supported by Izmir Katip Celebi University Scientific Research Projects Coordination Unit: Poject number 2017-ÖNAP–MÜMF-0002.

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Authors and Affiliations

  1. Department of Mechatronics Engineering, Bursa Technical University, Yildirim, 16330, Bursa, Turkey

    Ahmet Mert

  2. Department of Biomedical Engineering, Izmir Katip Celebi University, 34320, Cigli, Izmir, Turkey

    Aydin Akan

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  1. Ahmet Mert
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  2. Aydin Akan
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Correspondence to Ahmet Mert.

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Mert, A., Akan, A. Seizure onset detection based on frequency domain metric of empirical mode decomposition. SIViP 12, 1489–1496 (2018). https://doi.org/10.1007/s11760-018-1304-y

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  • DOI: https://doi.org/10.1007/s11760-018-1304-y

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