Model-based spike detection of epileptic EEG data

Sensors (Switzerland)

Volumn 13, Issue 9, 2013, Pages 12536-12547

  Liu, Yung Chun ^a   Lin, Chou Ching K ^b   Tsai, Jing Jane ^b   Sun, Yung Nien ^a  

^a NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

^b NATIONAL CHENG KUNG UNIVERSITY HOSPITAL   (Taiwan)

Author keywords

Epilepsy; Nonlinear energy operator; Slow wave; Spike classification; Spike detection

Indexed keywords

ADAPTIVE BOOSTING;

ADA BOOST CLASSIFIERS; AUTOMATIC SPIKE DETECTION; CONVENTIONAL METHODS; ELECTROENCEPHALOGRAM (EEG) DATUM; ENERGY OPERATORS; EPILEPSY; SLOW WAVE; SPIKE DETECTION;

ELECTROENCEPHALOGRAPHY;

EID: 84884369365     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s130912536     Document Type: Article

References (24)

1. Chatrian, G.E.; Bergamini, L.; Dondey, M.; Klass, D.W.; Lennox-Buchthal, M.; Petersen, I. A glossary of terms most commonly used by clinical electroencephalographers. Electroencephalogr. Clin. Neurophysiol. 1974, 37, 538-548.
2. Löfhede, J.; Seoane, F.; Thordstein, M. Textile electrodes for EEG recording-A pilot study. Sensors 2012, 12, 16907-16919.
3. Wilson, S.B.; Emerson, R. Spike detection: A review and comparison of algorithms. Clin. Neurophysiol. 2002, 113, 1873-1881.
4. Halford, J.J. Computerized epileptiform transient detection in the scalp electroencephalogram: Obstacles to progress and the example of computerized ECG interpretation. Clin. Neurophysiol. 2009, 120, 1909-1915.
5. Acir, N.; Oztura, I.; Kuntalp, M.; Baklan, B.; Guzelis, C. Automatic detection of epileptiform events in EEG by a three-stage procedure based on artificial neural networks. IEEE Trans. Biomed. Eng. 2005, 52, 30-40.
6. Ktonas, P.Y. Automated Spike and Sharp Wave (SSW) Detection. In Methods of Analysis of Brain Electrical and Magnetic Signals (Handbook of Electroencephalography and Clinical Neurophysiology, Rev Series 1); Gevins, A.S., Remond, A., Eds.; Elsevier: Amsterdam, The Netherlands, 1987; pp. 211-241.
7. Ji, Z.; Sugi, T.; Goto, S.; Wang, X.; Ikeda, A.; Nagamine, T.; Shibasaki, H.; Nakamura, M. An automatic spike detection system based on elimination of false positives using the large-area context in the scalp EEG. IEEE Trans. Biomed. Eng. 2011, 58, 2478-2488.
8. Gotman, J.; Gloor, P. Automatic recognition and quantification of interictal epileptic activity in the human scalp EEG. Electroencephalogr. Clin. Neurophysiol. 1976, 41, 513-529.
9. Exarchos, T.P.; Tzallas, A.T.; Fotiadis, D.I.; Konitsiotis, S.; Giannopoulos, S. EEG transient event detection and classification using association rules. IEEE Trans. Inf. Technol. Biomed. 2006, 10, 451-457.
10. Adjouadi, M.; Cabrerizo, M.; Ayala, M.; Sanchez, D.; Yaylali, I.; Jayakar, P.; Barreto, A. A new mathematical approach based on orthogonal operators for the detection of interictal spikes in epileptogenic data. Biomed. Sci. Instrum. 2004, 40, 175-180.
11. Indiradevi, K.P.; Elias, E.; Sathidevi, P.S.; Dinesh, N.S.; Radhakrishnan, K. A multi-level wavelet approach for automatic detection of epileptic spikes in the electroencephalogram. Comput. Biol. Med. 2008, 38, 805-816.
12. Tzallas, A.T.; Karvelis, P.S.; Katsis, C.D.; Fotiadis, D.I.; Giannopoulos, S.; Konitsiotis, S. A method for classification of transient events in EEG recordings: application to epilepsy diagnosis. Methods Inf. Med. 2006, 45, 610-621.
13. Güler, I.; Übeyli, E.D. Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. J. Neurosci. Methods 2005, 148, 113-121.
14. Hellmann, G. Multifold features determine linear equation for automatic spike detection applying neural nin interictal ECoG. Clin. Neurophysiol. 1999, 110, 887-894.
15. Dingle, A.A.; Jones, R.D.; Carroll, G.J.; Fright, W.R. A multistage system to detect epileptiform activity in the EEG. IEEE Trans. Biomed. Eng. 1993, 40, 1260-1268.
16. Kalayci, T.; Ozdamar, O. Wavelet preprocessing for automated neural network detection of EEG spikes. IEEE Eng. Med. Biol. Mag. 1995, 14, 160-166.
17. Pang, C.C.; Upton, A.R.; Shine, G.; Kamath, M.V. A comparison of algorithms for detection of spikes in the electroencephalogram. IEEE Trans. Biomed. Eng. 2003, 50, 521-526.
18. Adjouadi, M.; Sanchez, D.; Cabrerizo, M.; Ayala, M.; Jayakar, P.; Yaylali, I.; Barreto, A. Interictal spike detection using the Walsh transform. IEEE Trans. Biomed. Eng. 2004, 51, 868-872.
19. Maragos, P.; Kaiser, J.F.; Quatieri, T.F. On amplitude and frequency demodulation using energy operators. IEEE Trans. Signal Process. 1993, 41, 1532-1550.
20. Kaiser, J.F. On a Simple Algorithm to Calculate the 'Energy' of a Signal. In Proceedings of the IEEE International Conference on Acoustics, Speech, Signal Processing, Albuquerque, NM, USA, 3-6 April 1990; pp. 381-384.
21. Choi, J.H.; Kim, T. Neural action potential detector using multi-resolution TEO. Electron. Lett. 2002, 38, 541-543.
22. Freund, Y.; Schapire, R.E. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 1997, 55, 119-139.
23. Duda, R.O.; Hart, P.E.; Stork, D.G. Pattern Classification, 2nd ed.; John Wiley & Sons: New York, NY, USA, 2001; pp. 476-480.
24. Provost, F.; Kohavi, R. Glossary of terms. Mach. Learn. 1998, 30, 271-274.