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.
Buy single article
Instant access to the full article PDF.
Price excludes VAT (USA)
Tax calculation will be finalised during checkout.
Witte, H., Iasemidis, L. D., & Litt, B. (2003). Special issue on epileptic seizure prediction. IEEE Transactions on Biomedical Engineering, 50, 537–539.
Misulis K. E. (2013). Atlas of EEG, seizure semiology, and management (2nd ed.). Oxford: Oxford University Press. ISBN-13: 978-0199985906.
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.
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.
Übeyli, E. D., & Güler, I. (2007). Features extracted by eigenvector methods for detecting variability of EEG signals. Pattern Recognition Letters, 28, 592–603.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Zaccaria G. M. (2011). Sonification of EEG signals—A study on alpha band instantaneous coherence. Master Thesis, Universitat Pompeu Fabra.
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.
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.
Temko, A., Marnane, W., Boylan, G., & Lightbody, G. (2015). Clinical implementation of a neonatal seizure detection algorithm. Decision Support Systems, 70, 86–96.
Wu, Z., & Huang, N. E. (2009). Ensemble empirical mode decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1, 1–41.
Martínez, A. M., & Kak, A. C. (2001). PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, 228–233.
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.
Keys, R. (1981). Cubic convolution interpolation for digital image processing. IEEE Transactions on Acoustics, Speech, and Signal Processing, 29, 1153–1160.
Yadav, R., Swamy, M., & Agarwal, R. (2012). Model-based seizure detection for intracranial EEG recordings. IEEE Transactions on Biomedical Engineering, 59, 1419–1428.
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.
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.
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.
Rights and permissions
About this article
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
- Electroencephalogram (EEG)
- Ensemble empirical mode decomposition (EEMD)
- Linear discriminant analysis (LDA)