Skip to main content

Advertisement

SpringerLink
Log in

The Effect of Multiscale PCA De-noising in Epileptic Seizure Detection

  • Patient Facing Systems
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

In this paper we describe the effect of Multiscale Principal Component Analysis (MSPCA) de-noising method in terms of epileptic seizure detection. In addition, we developed a patient-independent seizure detection algorithm using Freiburg EEG database. Each patient contains datasets called “ictal” and “interictal”. Window length of 16 s was applied to extract EEG segments from datasets of each patient. Furthermore, Power Spectral Density (PSD) of each EEG segment was estimated using different spectral analysis methods. Afterwards, these values were fed as input to different machine learning methods that were responsible for seizure detection. We also applied MSPCA de-noising method to EEG segments prior to PSD estimation to determine if MSPCA can further enhance the classifiers’ performance. The MSPCA drastically improved both the sensitivity and the specificity, increasing the overall accuracy of all three classifiers up to 20 %. The best overall detection accuracy (99.59 %) was achieved when Eigenvector analysis was used for frequency estimation, and C4.5 as a classifier. The experiment results show that MSPCA is an effective de-noising method for improving the classification performance in epileptic seizure detection.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. M. D’Alessandro, R. Esteller, G. Vachtsevanos, A. Hinson, J. Echauz and B. Litt, “Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: a report of four patients,” IEEE Transactions on Biomedical Engineering 50 (5), pp. 603–615, 2003.

    Article  Google Scholar 

  2. R. Begg, D. T. H. Lai and M. Palaniswami, Computational Intelligence in Biomedical Engineering, Boca Raton: CRC Press, 2008.

    MATH  Google Scholar 

  3. S. B. Akben, A. Subasi and D. Tuncel, “Analysis of EEG Signals under Flash Stimulation for Migraine and Epileptic Patients,” Journal of Medical Systems, vol. 35, no. 3, pp. 437–443, 2011.

    Article  Google Scholar 

  4. M. K. Kiymik, A. Subasi and H. R. Ozcalik, “Neural networks with periodogram and autoregressive spectral analysis methods in detection of epileptic seizure,” Journal of Medical Systems, vol. 28, no. 6, pp. 511–523, 2004.

    Article  Google Scholar 

  5. K. C. Chua, V. Chandran, U. R. Acharya and C. M. Lim, “Application of Higher Order Spectra to Identify Epileptic EEG,” Journal of Medical Systems, vol. 35, pp. 1563–1571, 2011.

    Article  Google Scholar 

  6. S. N. Oğulata, C. Şahin and R. Erol, “Neural Network-Based Computer-Aided Diagnosis in Classification of Primary Generalized Epilepsy by EEG Signals,” Journal of Medical Systems, vol. 33, pp. 107–112, 2009.

    Article  Google Scholar 

  7. M. Faezipour, A. Saeed, S. C. Bulusu, M. Nourani and H. Minn, “A Patient-Adaptive Profiling Scheme for ECG Beat Classification,” IEEE Transactions On Information Technology In Biomedicine, vol. 14, no. 5, pp. 1153–1165, 2010.

    Article  Google Scholar 

  8. J. Mateo, A. M. Torres, C. Soria and J. L. Santos, “A method for removing noise from continuous brain signal recordings,” Computers and Electrical Engineering, 2012.

  9. L. Sornmo and P. Laguna, Bioelectrical Signal Processing in Cardiac and Neurological Applications, Elsevier Academic Press, 2005.

  10. J. Bronzino, The biomedical engineering handbook, 2nd ed., CRC Press, Springer, 2000.

    Google Scholar 

  11. R. M. Rangayyan, Biomedical signal analysis: a case-study approach, IEEE Press Series in Biomedical Engineering, 2002.

  12. N. P. Castellanos and V. A. Makarov, “Recovering EEG brain signals: artifact suppression with wavelet enhanced independent component analysis,” Journal of Neuroscience Methods, vol. 158, no. 2, pp. 300–312, 2006.

    Article  Google Scholar 

  13. D. L. Donoho, “Denoising by soft thresholding,” IEEE Transactions on Information Theory, pp. 613–627, 1995.

  14. P. K. Sadasivan and D. N. Dutt, “SVD based technique for noise reduction in electroencephalographic signals,” Signal Processing, vol. 55, no. 2, pp. 179–89, 1996.

    Article  MATH  Google Scholar 

  15. N. Ille, P. Berg and M. Scherg, “Artifact correction of the ongoing eeg using spatial filters based on artifact and brain signal topographies,” Clinical Neurophysilogy, vol. 19, no. 2, pp. 113–124, 2002.

    Article  Google Scholar 

  16. T. Lagerlund, F. Sharbrough and N. Busacker, “Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition,” Clinical Neurophysiology, vol. 14, no. 1, pp. 73–82, 1997.

    Article  Google Scholar 

  17. T. P. Jung, C. Humphries, T. W. Lee, S. Makeig, M. J. McKeown, V. Iragui and T. J. Sejnowski, “Extended ICA Removes Artifacts from Electroencephalographic Recordings,” Advances in Neural Information Processing Systems, vol. 10, pp. 894–900, 1998.

    Google Scholar 

  18. L. Albera, A. Kachenoura, P. Comon, A. Karfoul, F. Wendling, L. Senhadji and I. Merlet, “ICA-based EEG denoising: a comparative analysis of fifteen methods,” Bulletin of the Polish Academy of Sciences - Technical Sciences, vol. 60, no. 3, pp. 407–418, 2012.

    Article  Google Scholar 

  19. M. T. Akhtar, W. Mitsuhashi and C. J. James, “Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data,” Signal Processing, pp. 401–416, 2012.

  20. R. Romo-Vazquez, R. Ranta, V. Louis-Dorr and D. Maquin, “Ocular Artifacts Removal In Scalp EEG: Combining ICA And Wavelet Denoising,” in 5th International Conference on Physics in Signal and Image Processing, Mulhouse, France, 2007.

    Google Scholar 

  21. B. R. Bakshi, “Multiscale PCA with Application to Multivariate Statistical Process Monitoring,” AlChE, vol. 44, no. 7, pp. 1596–1610, 1998.

    Article  Google Scholar 

  22. Uni-Freiburg, “Seizure Prediction Project Freiburg,” 2011. [Online]. Available: https://epilepsy.uni-freiburg.de/freiburg-seizure-prediction-project/eeg-database. [Accessed 2 October 2011].

  23. C. Bigan, “A recursive time-frequency processing method for neural networks recognition of EEG seizures,” in Neural Networks and Expert Systems in Medicine and Healthcare, E. C. Ifeachor, A. Sperduti and A. Starita, Eds., Singapore, World Scientific, 1998.

    Google Scholar 

  24. J. Kevric, Classification of EEG signals for epileptic seizure detection using different signal processing and machine learning methods. (Master’s Thesis), Sarajevo: International Burch University, 2012.

    Google Scholar 

  25. Q. Yuan, W. Zhou, Y. Liu and J. Wang, “Epileptic seizure detection with linear and nonlinear features,” Epilepsy & Behavior, vol. 24, pp. 415–421, 2012.

    Article  Google Scholar 

  26. A. Aarabi, R. Fazel-Rezai and Y. Aghakhani, “A fuzzy rule-based system for epileptic seizure detection in intracranial EEG,” Clinical Neurophysiology, vol. 120, pp. 1648–1657, 2009.

    Article  Google Scholar 

  27. A. Shoeb and J. Guttag, “Application of Machine Learning To Epileptic Seizure Detection,” in Proc. of the 27th International Conference on Machine Learning, Haifa, Israel, 2010.

  28. “CHB-MIT Scalp EEG Database,” [Online]. Available: http://physionet.org/physiobank/database/chbmit/.

  29. L. M. Patnaik and O. K. Manyam, “Epileptic EEG detection using neural networks and post-classification,” Computer Methods and Programs in Biomedicine, pp. 100–109, 2008.

  30. E. C.-P. Chua, K. Patel, M. Fitzsimons and C. J. Bleakley, “Improved patient specific seizure detection during pre-surgical evaluation,” Clinical Neurophysiology, vol. 122, pp. 672–679, 2011.

    Article  Google Scholar 

  31. R. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David and C. Elger, “Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state,” Physical Review E, vol. 64, no. 6, 2001.

  32. Y. Kumar, M. L. Dewal and R. S. Anand, “Relative Wavelet Energy and Wavelet Entropy Based Epileptic Brain Signals Classification,” Biomedical Engineering Letters, pp. 147–157, 2012.

  33. S. Ghosh-Dastidar, H. Adeli and N. Dadmehr, “Principal Component Analysis-Enhanced Cosine Radial Basis Function Neural Network for Robust Epilepsy and Seizure Detection,” IEEE Transactions on Biomedical Engineering, pp. 512–518, 2008.

  34. H. Ocak, “Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy,” Expert Systems with Application, pp. 2027–2036, 2009.

  35. S. Koçer and M. R. Canal, “Classifying Epilepsy Diseases Using Artificial Neural Networks and Genetic Algorithm,” Journal of Medical Systems, vol. 35, pp. 489–498, 2011.

    Article  Google Scholar 

  36. A. Subasi and M. I. Gursoy, “Comparison of PCA, ICA and LDA in EEG signal classification using DWT and SVM,” Expert Systems with Applications, vol. 37, pp. 8659–8666, 2010.

    Article  Google Scholar 

  37. V. Srinivasan, C. Eswaran and C. Sriraam, “Approximate entropy-based epileptic EEG detection using artificial neural networks,” IEEE Transactions On Information Technology In Biomedicine, pp. 288–295, 2007.

  38. A. T. Tzallas, M. G. Tsipouras and D. I. Fotiadis, “Epileptic Seizure Detection in EEGs Using Time–Frequency Analysis,” IEEE Transactions on Information Technology in Biomedicine, pp. 703–710, 2009.

  39. S. M. S. Alam and M. I. H. Bhuiyan, “Detection of Epileptic Seizures using Chaotic and Statistical Features in the EMD Domain,” in Annual IEEE India Conference, 2011.

  40. E. Sezer, H. Işik and E. Saracoğlu, “Employment and Comparison of Different Artificial Neural Networks for Epilepsy Diagnosis from EEG Signals,” Journal of Medical Systems, vol. 36, pp. 347–362, 2012.

    Article  Google Scholar 

  41. S. Raghunathan, A. Jaitli and P. P. Irazoqui, “Multistage seizure detection techniques optimized for low-power hardware platforms,” Epilepsy & Behavior, vol. 22, pp. S61-S68, 2011.

    Article  Google Scholar 

  42. S. L. Marple, Digital Spectral Analysis with Applications, Englewood Cliffs, NJ: Prentice-Hall, 1987.

    Google Scholar 

  43. A. Alkan and M. K. Kiymik, “Comparison of AR and Welch Methods in Epileptic Seizure Detection,” Journal of Medical Systems, vol. 30, pp. 413–419, 2006.

    Article  Google Scholar 

  44. J. L. Semmlow, Biosignal and biomedical image processing: MATLAB-based applications, New York: Marcel Dekker, Inc., 2004.

    Book  Google Scholar 

  45. C. W. Anderson, E. A. Stoltz and S. Shamsunder, “Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks,” IEEE Trans. Biomed. Eng., vol. 45, pp. 277–286, 1998.

    Article  Google Scholar 

  46. B. H. Jansen, J. R. Bourne and J. W. Ward, “Autoregressive estimation of short seg- ment spectra for computerized EEG analysis,” IEEE Trans. Biomed. Eng., vol. 28, pp. 630–638, 1981.

    Article  Google Scholar 

  47. M. Aminghafari, N. Cheze and J.-M. Poggi, “Multivariate denoising using wavelets and principal component analysis,” Computational Statistics & Data Analysis, vol. 50, pp. 2381–2398, 2006.

    Article  MathSciNet  MATH  Google Scholar 

  48. M. Minsky and S. A. Papert, Perceptrons: An Introduction to Computational Geometry (expanded edition), Cambridge, MA: MIT Press, 1988/1969.

  49. M. Dunham, Data Mining: Introductory and Advanced Topics, Upper Saddle River, NJ: Prentice Hall, 2003.

    Google Scholar 

  50. M. W. Berry and M. Browne, Eds., Lecture notes in Data Mining, Singapore: World Scientific, 2006.

  51. B. V. Dasarathy, “NN concepts and techniques. An introductory survey,” in Nearest Neighbour Norm: NN Pattern Classification Techniques, B. V. Dasarathy, Ed., Los Alamitos, CA, IEEE Computer Society Press, 1991, pp. 1–30.

    Google Scholar 

  52. R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, New York: John Wiley and Sons, 2000.

    Google Scholar 

  53. G. G. Enas and S. C. Choi, “Choice of the smoothing parameter and efficiency of k-nearest neighbor classification,” Computers & Mathematics with Applications, pp. 235–244, 1986.

  54. I. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, San Francisco, CA: Morgan Kaufmann Publishers (Elsevier), 2005.

  55. R. Zhang, G. McAllister, B. Scotney, S. McClean and G. Houston, “Combining Wavelet Analysis and Bayesian Networks for the Classification of Auditory Brainstem Response,” IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 3, pp. 458–467, 2006.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, International Burch University, Sarajevo, 71000, Bosnia and Herzegovina

    Jasmin Kevric & Abdulhamit Subasi

Authors
  1. Jasmin Kevric
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdulhamit Subasi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jasmin Kevric.

Additional information

This article is part of the Topical Collection on Patient Facing Systems

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kevric, J., Subasi, A. The Effect of Multiscale PCA De-noising in Epileptic Seizure Detection. J Med Syst 38, 131 (2014). https://doi.org/10.1007/s10916-014-0131-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-014-0131-0

Keywords

Search

Navigation