Skip to main content

Visualization and Sonification of Long-Term Epilepsy Electroencephalogram Monitoring

Journal of Medical and Biological Engineering volume 38, pages 943–952 (2018)Cite this article

Abstract

Long-term electroencephalogram (EEG) monitoring is effective for epilepsy diagnosis. However, it also takes a lot of time for clinicians to correctly interpret the long-time recordings. Real-time computer-based EEG monitoring and classification systems have attracted recently the attention of researchers to help clinicians locate online possible epileptic-form EEG signals. In this paper, we first present an accurate and fast EEG classification algorithm that can recognize three types of EEG signals: normal, spike, and seizure. 16-channel bipolar EEG recordings of epilepsy patients are preprocessed, segmented, and ensemble empirical mode decomposed (EEMD) into intrinsic mode functions (IMFs). Features are extracted and linear discriminant analysis (LDA) is applied to train two classifiers: one is for seizure and non-seizure discrimination, and the other is for normal and spike discrimination. In order to furthermore help the clinicians, the results of LDA are visualized and sonified. The changes of the discriminant in the LDA on continuous EEG segments are backtracked to each feature, and thus to each EEG channel. Accordingly, contours of the changes in EEG channels are depicted. At the same time, sinusoidal waves in 440 or 880 Hz are played when EEG segments are classified into spike or seizure respectively. In the experiment, EEG recordings of six subjects (two normal and four seizure patients) are examined. The experiment result shows that the accuracy of the proposed epileptic EEG classification algorithm is relatively high. In addition, the visualization and sonification algorithms of epileptic-form EEG may greatly help clinicians localize the focus of seizure and nurses take care of seizure patients, immediately.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  1. Witte, H., Iasemidis, L. D., & Litt, B. (2003). Special issue on epileptic seizure prediction. IEEE Transactions on Biomedical Engineering, 50, 537–539.

    Article  Google Scholar 

  2. Misulis K. E. (2013). Atlas of EEG, seizure semiology, and management (2nd ed.). Oxford: Oxford University Press. ISBN-13: 978-0199985906.

    Chapter  Google Scholar 

  3. Shen, C. P., Lin, J. W., Lin, F. S., Lam, A. Y. Y., Chen, W., Zhou, W., et al. (2017). GA-SVM modeling of multiclass seizure detector in epilepsy analysis system using cloud computing. Soft Computing, 21, 2139–2149. https://doi.org/10.1007/s00500-015-1917-9.

    Article  Google Scholar 

  4. Güler, I., & Übeyli, E. D. (2005). Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. Journal of Neuroscience Methods, 148, 113–121.

    Article  Google Scholar 

  5. Übeyli, E. D., & Güler, I. (2007). Features extracted by eigenvector methods for detecting variability of EEG signals. Pattern Recognition Letters, 28, 592–603.

    Article  Google Scholar 

  6. Tzallas, A. T., Tsipouras, M. G., & Fotiadis, D. I. (2009). Epileptic seizure detection in EEGs using time frequency analysis. IEEE Transactions on Information Technology in Biomedicine, 13, 703–710.

    Article  Google Scholar 

  7. Ghosh-Dastidar, S., & Adeli, H. (2008). Principle component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection. IEEE Transactions on Biomedical Engineering, 55, 512–518.

    Article  Google Scholar 

  8. Chen W., Lam Y. Y., Shen C. P., Sung H. Y., Lin J. W., Chiu M. J., et al. (2013). Ultra-fast epileptic seizure detection using EMD based on multichannel electroencephalogram. In Proceedings of the IEEE International Conference Bioinformatics and Bioengineering, pp. 1–4.

  9. Davey, B. L., Fright, W. R., Carroll, G. J., & Jones, R. D. (1989). Expert system approach to detection of epileptiform activity in the EEG. Medical & Biological Engineering & Computing, 27, 365–370.

    Article  Google Scholar 

  10. Nonclercq, A., Foulon, M., Verheulpen, D., Cock, C. D., Buzatu, M., Mathys, P., et al. (2009). Spike detection algorithm automatically adapted to individual patients applied to spike-and-wave percentage quantification. Neurophysiologie Clinique, 39, 123–131.

    Article  Google Scholar 

  11. Ko, C. W., & Chung, H. W. (2000). Automatic spike detection via an artificial neural network using raw EEG data: Effects of data preparation and implications in the limitations of online recognition. Clinical Neurophysiology, 111, 477–481.

    Article  Google Scholar 

  12. Indiradevi, K. P., Elias, E., Sathidevi, P. S., Nayak, S. D., & Radhakrishnan, K. (2008). A multi-level wavelet approach for automatic detection of epileptic spikes in the electroencephalogram. Computers in Biology and Medicine, 38, 805–816.

    Article  Google Scholar 

  13. Acir, N., & Guzelis, C. (2004). Automatic spike detection in EEG by a twostage procedure based on support vector machines. Computers in Biology and Medicine, 34, 561–575.

    Article  Google Scholar 

  14. Oikonomou, V. P., Tzallas, A. T., & Fotiadis, D. I. (2007). A Kalman-filter-based methodology for EEG spike enhancement. Computer Methods and Programs in Biomedicine, 85, 101–108.

    Article  Google Scholar 

  15. Lucia, M. D., Fritschy, J., Dayan, P., & Holder, D. (2008). A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis. Medical & Biological Engineering & Computing, 46, 263–272.

    Article  Google Scholar 

  16. Inan, Z. H., & Kuntalp, M. (2007). A study on fuzzy c-means clustering-based systems in automatic spike detection. Computers in Biology and Medicine, 37, 1160–1166.

    Article  Google Scholar 

  17. Zaccaria G. M. (2011). Sonification of EEG signals—A study on alpha band instantaneous coherence. Master Thesis, Universitat Pompeu Fabra.

  18. Baier G., & Hermann T. (2004). The sonification of rhythms in human electroencephalogram. In Proceedings of the International Conference on Auditory Display, Sydney, Australia, July 6–9.

  19. Vialatte, F. B., Dauwels, J., Musha, T., & Cichocki, A. (2012). Audio representations of multi-channel EEG: A new tool for diagnosis of brain disorders. American Journal of Neurodegenerative Disease, 1, 292–304.

    Google Scholar 

  20. Temko, A., Marnane, W., Boylan, G., & Lightbody, G. (2015). Clinical implementation of a neonatal seizure detection algorithm. Decision Support Systems, 70, 86–96.

    Article  Google Scholar 

  21. Wu, Z., & Huang, N. E. (2009). Ensemble empirical mode decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1, 1–41.

    Article  Google Scholar 

  22. Martínez, A. M., & Kak, A. C. (2001). PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, 228–233.

    Article  Google Scholar 

  23. Niedermeyer E., & da Silva F. L. (2004). Electroencephalography: Basic principles, clinical applications, and related fields (5th ed.). London: Lippincott Williams and Wilkins. ISBN-13: 978-0781751261.

  24. Keys, R. (1981). Cubic convolution interpolation for digital image processing. IEEE Transactions on Acoustics, Speech, and Signal Processing, 29, 1153–1160.

    Article  MathSciNet  Google Scholar 

  25. Yadav, R., Swamy, M., & Agarwal, R. (2012). Model-based seizure detection for intracranial EEG recordings. IEEE Transactions on Biomedical Engineering, 59, 1419–1428.

    Article  Google Scholar 

  26. Lucia, M., Fritschy, J., Dayan, P., & Holder, D. (2008). A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis. Medical & Biological Engineering & Computing, 46, 263–272.

    Article  Google Scholar 

  27. Halforda, J. J., Schalkoffb, R. J., Zhoub, J., Benbadisc, S. R., Tatumd, W. O., Turnera, R. P., et al. (2013). Standardized database development for EEG epileptiform transient detection: EEGnet scoring system and machine learning analysis. Journal of Neuroscience Methods, 212, 308–316.

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported in part by Ministry of Science and Technology of Taiwan (R.O.C.) under Grants MOST 105-2221-E-029-020-MY2 and MOST 106-2420-H-029-003-MY2.

Author information

Authors and Affiliations

  1. Department of Information Management, Tunghai University, Taichung, Taiwan

    Jeng-Wei Lin

  2. Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan

    Wei Chen, Chia-Ping Shen, Ming-Jang Chiu & Feipei Lai

  3. Department of Neurology, National Taiwan University Hospital, College of Medicine, Taipei, Taiwan

    Ming-Jang Chiu

  4. Graduate Institute of Brain and Mind Sciences, National Taiwan University, Taipei, Taiwan

    Ming-Jang Chiu

  5. Department of Psychology, National Taiwan University, Taipei, Taiwan

    Ming-Jang Chiu

  6. Department of Neurology, National Taiwan University Hospital Yun-Lin Branch, Yun-Lin, Taiwan

    Yi-Hui Kao

  7. Department of Electronics Engineering, National Taiwan University, Taipei, Taiwan

    Feipei Lai

  8. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan

    Feipei Lai

  9. Tensor Learning Unit, RIKEN AIP, Tokyo, Japan

    Qibin Zhao

  10. School of Automation, Guangdong University of Technology, Guangzhou, China

    Qibin Zhao

  11. Skolkovo Institute of Science and Technology (Skoltech), Moscow, 143026, Russia

    Andrzej Cichocki

  12. Nicolaus Copernicus University (UMK), Toruń, Poland

    Andrzej Cichocki

  13. RIKEN Brain Science Institute, Wako-Shi, Saitama, 351-0198, Japan

    Andrzej Cichocki

  14. No. 7, Chung-Shan S. Rd, Taipei, 10002, Taiwan, ROC

    Ming-Jang Chiu

Authors
  1. Jeng-Wei Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chia-Ping Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ming-Jang Chiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yi-Hui Kao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Feipei Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Qibin Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Andrzej Cichocki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ming-Jang Chiu.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lin, JW., Chen, W., Shen, CP. et al. Visualization and Sonification of Long-Term Epilepsy Electroencephalogram Monitoring. J. Med. Biol. Eng. 38, 943–952 (2018). https://doi.org/10.1007/s40846-017-0358-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40846-017-0358-6

Keywords

  • Electroencephalogram (EEG)
  • Multi-channel
  • Epilepsy
  • Visualization
  • Sonification
  • Ensemble empirical mode decomposition (EEMD)
  • Linear discriminant analysis (LDA)