Energy Distribution of EEG Signals: EEG Signal Wavelet-Neural Network Classifier
In this paper, a wavelet-based neural network (WNN) classifier for recognizing EEG signals is implemented and tested under three sets EEG signals (healthy subjects, patients with epilepsy and patients with epileptic syndrome during the seizure). First, the Discrete Wavelet Transform (DWT) with the Multi-Resolution Analysis (MRA) is applied to decompose EEG signal at resolution levels of the components of the EEG signal (δ, θ, α, β and γ) and the Parseval-s theorem are employed to extract the percentage distribution of energy features of the EEG signal at different resolution levels. Second, the neural network (NN) classifies these extracted features to identify the EEGs type according to the percentage distribution of energy features. The performance of the proposed algorithm has been evaluated using in total 300 EEG signals. The results showed that the proposed classifier has the ability of recognizing and classifying EEG signals efficiently.
[1] S. Tong and N.V.Thacor, Engineering in Medicine & Biology-
Quantitative EEG Analysis Methods and Clinical Applications,
Boston/London: Artech House, 2009.
[2] L. M. Patnaika and O. K. Manyamb, "Epileptic EEG detection using
neural networks and post-classification" Computer methods and
programs in biomedicine- Elsevier, vol. 91, pp. 100-109. 2008.
[3] A. Prochazka, J. Kukal and O.Vysata, "Wavelet transform use for
feature extraction and EEG signal segments classification," in Proc.
2008 IEEE Communications, Control and Signal Processing, 3rd
International Symposium, pp. 719 - 722.
[4] V. Bostanov and B. Kotchoubey, "Recognition of affective prosody:
Continuous wavelet measures of event-related brain potentials to
emotional exclamations," Psychophysiology, vol. 41, pp. 259-268, 2004.
[5] M. Murugappan, M. Rizon, R. Nagarajan, S. Yaacob, I. Zunaidi, and D.
Hazry, "EEG Feature Extraction for Classifying Emotions using FCM
and FKM," Int. Journal of Computers and Comunications, vol. 1, pp.
21-25, 2007.
[6] C. Wang, J. Zou, J. Zhang, M. Wang and R. Wang, "Feature extraction
and recognition of epileptiform activity in EEG by combining PCA with
ApEn", Biomedical and Life Sciences- Springer, vol. 4, pp. 233-240,
2010.
[7] L. Guo, D. Rivero, J. A. Seoane and A. Pazos, "Classification of EEG
Signals Using Relative Wavelet Energy and Artificial Neural
Networks," GEC-09, Shanghai, China, 2009.
[8] M. Akin, M. A. Arserim, M. K. Kiymik and I. Turkoglu, "A New
Approach For Diagnosing Epilepsy By Using Wavelet Transform And
Neural Networks," in Proc. 2001 IEEE/EMBS 23rd Annual Conference,
pp. 1596 - 1599.
[9] H. S. Liu, T. Zhang and F. S. Yang, "A Multistage, Multimethod
Approach for Automatic Detection and Classification of Epileptiform
EEG," IEEE Transactions on Biomedical Engineering, vol. 49, pp. 1557
- 1566, 2002.
[10] P. Jahankhani, V. Kodogiannis, and K. Revett, "EEG Signal
Classification Using Wavelet Feature Extraction and Neural Networks,"
in Proc. 2003 International Symposium on Modern Computing, pp. 120
- 124.
[11] A. A. Mashakbeh, "Analysis Of Electroencephalogram To Detect
Epilepsy," Int. Journal Of Academic Research, vol. 2, pp. 63-69, 2010.
[12] M. M. Shaker, "EEG Waves Classifier using Wavelet Transform and
Fourier Transform," Int. Journal of Biological and Life Sciences, vol. 1,
pp. 85-90, 2005.
[13] I. Omerhodzic, E. Causevic, K. Dizdarevic, S. Avdakovic, M. Music, M.
Kusljugic, E. Hajdarpasic, N. Kadic, "First neurosurgical experience
with the wavelet based EEG in diagnostic of concussion," 11th Congress
of Neurosurgeons of Serbia, Nis, Serbia, 2008. Abstract book p. 18.
[14] L. M. Patnaik, O. K. Manyam, "Epileptic EEG detection using neural
networks and post-classification". Comput Methods Programs Biomed.
2008 Aug;91(2):100-9.
[15] A. Subasi, E. Erçelebi, "Classification of EEG signals using neural
network and logistic regression". Comput Methods Programs Biomed.
2005 May;78(2):87-99.
[16] K. Asaduzzaman, M. B. Reaz, F. Mohd-Yasin, K. S. Sim, M. S.
Hussain, "A study on discrete wavelet-based noise removal from EEG
signals", Adv Exp Med Biol. 2010;680:593-9.
[17] H. Adeli, Z. Zhou, N. Dadmehr. "Analysis of EEG records in an
epileptic patient using wavelet transform", J Neurosci Methods. 2003
Feb 15;123(1):69-87.
[18] L. Guo, D. Rivero, J. Dorado, J. R. Rabunal, A. Pazos, "Automatic
epileptic seizure detection in EEGs based on line length feature and
artificial neural networks", J Neurosci Methods. 2010 Aug
15;191(1):101-9.
[19] A. S. Zandi, M. Javidan, G. A. Dumont, R. Tafreshi, "Automated realtime
epileptic seizure detection in scalp EEG recordings using an
algorithm based on wavelet packet transform", IEEE Trans Biomed Eng.
2010 Jul;57(7):1639-51.
[20] P. Mirowski, D. Madhavan, Y. Lecun, R. Kuzniecky, Classification of
patterns of EEG synchronization for seizure prediction. Clin
Neurophysiol. 2009 Nov;120(11):1927-40.
[21] A. S. Zandi, G. A. Dumont, M. Javidan, R. Tafreshi, B. A. MacLeod, C.
R. Ries, E. A. Puil, "A novel wavelet-based index to detect epileptic
seizures using scalp EEG signals", Conf Proc IEEE Eng Med Biol Soc.
2008;2008:919-22.
[22] A. Subasi, A. Alkan, E. Koklukaya, M. K. Kiymik, "Wavelet neural
network classification of EEG signals by using AR model with MLE
preprocessing", Neural Netw. Sep 2005, vol. 18(7), 985-997.
[23] S. J. Schiff, A. Aldroubi, M. Unser, S. Sato, "Fast wavelet
transformation of EEG", Electroencephalogr Clin Neurophysiol. Dec
1994, vol. 91(6), 442-455.
[24] L. Senhadji, J. L. Dillenseger, F. Wendling, C. Rocha, A. Kinie,
"Wavelet analysis of EEG for three-dimensional mapping of epileptic
events", Ann Biomed Eng. Sep-Oct 1995, 23(5):543-52.
[25] C. E. D'Attellis, S. I. Isaacson, R. O. Sirne, "Detection of epileptic
events in electroencephalograms using wavelet analysis", Ann Biomed
Eng. Mar-Apr 1997, 25(2):286-93.
[26] H. Adeli, S. Ghosh-Dastidar, N. Dadmehr, "A wavelet-chaos
methodology for analysis of EEGs and EEG subbands to detect seizure
and epilepsy", IEEE Trans Biomed Eng. Feb 2007, 54(2):205-11.
[27] H. Leung, K. Schindler, A. Y. Chan, A. Y. Lau, K. L. Leung, E. H. Ng,
K. S. Wong, "Wavelet-denoising of electroencephalogram and the
absolute slope method: a new tool to improve electroencephalographic
localization and lateralization", Clin Neurophysiol. Jul 2009,
120(7):1273-81.
[28] I. Daubechies, Ten Lectures on Wavelets, Philadelphia: Society for
Industrial and Applied Mathematics, 1992.
[29] H. He and J. A. Starzyk, "A Self-Organizing Learning Array System for
Power Quality Classification Based on Wavelet Transform," IEEE
Transaction On Power Delivery, vol. 21(1), pp. 286-295, 2006.
[30] S. Mallat, A Wavelet Tour of Signal Processing, San Diego, CA:
Academic, 1998.
[31] S. Avdakovic and A. Nuhanovic, "Identifications and Monitoring of
Power System Dynamics Based on the PMUs and Wavelet Technique,"
International Journal of Energy and Power Engineering, vol. 3, pp. 202-
209, 2010.
[32] R. G. Andrzejak, K. Lehnertz, C. Rieke, F. Mormann, P. David, C. E.
Elger, "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, 64, 061907. [Online]
Available: http://epileptologie-bonn.de/cms/
[33] P. Settipalli, "Automated Classification Of Power Quality Disturbances
Using Signal Processing Technique and Neural Network", Ph.D.
dissertation, University of Kentucky; USA, 2007.
[34] S. Dreiseitl, L. Ohno-Machado, Logistic regression and artificial neural
network classification models: a methodology review, J. Biomed.
Inform. vol. 35, 352-359, 2002.
[35] I.A. Basheer, M. Hajmeer, Artificial neural networks: fundamentals,
computing, design, and application, J. Microbiol. Methods vol. 43, 3-31,
2000.
[36] B.B. Chaudhuri, U. Bhattacharya, Efficient training and improved
performance of multilayer perceptron in pattern classification,
Neurocomputing vol. 34, 11-27, 2000.
[1] S. Tong and N.V.Thacor, Engineering in Medicine & Biology-
Quantitative EEG Analysis Methods and Clinical Applications,
Boston/London: Artech House, 2009.
[2] L. M. Patnaika and O. K. Manyamb, "Epileptic EEG detection using
neural networks and post-classification" Computer methods and
programs in biomedicine- Elsevier, vol. 91, pp. 100-109. 2008.
[3] A. Prochazka, J. Kukal and O.Vysata, "Wavelet transform use for
feature extraction and EEG signal segments classification," in Proc.
2008 IEEE Communications, Control and Signal Processing, 3rd
International Symposium, pp. 719 - 722.
[4] V. Bostanov and B. Kotchoubey, "Recognition of affective prosody:
Continuous wavelet measures of event-related brain potentials to
emotional exclamations," Psychophysiology, vol. 41, pp. 259-268, 2004.
[5] M. Murugappan, M. Rizon, R. Nagarajan, S. Yaacob, I. Zunaidi, and D.
Hazry, "EEG Feature Extraction for Classifying Emotions using FCM
and FKM," Int. Journal of Computers and Comunications, vol. 1, pp.
21-25, 2007.
[6] C. Wang, J. Zou, J. Zhang, M. Wang and R. Wang, "Feature extraction
and recognition of epileptiform activity in EEG by combining PCA with
ApEn", Biomedical and Life Sciences- Springer, vol. 4, pp. 233-240,
2010.
[7] L. Guo, D. Rivero, J. A. Seoane and A. Pazos, "Classification of EEG
Signals Using Relative Wavelet Energy and Artificial Neural
Networks," GEC-09, Shanghai, China, 2009.
[8] M. Akin, M. A. Arserim, M. K. Kiymik and I. Turkoglu, "A New
Approach For Diagnosing Epilepsy By Using Wavelet Transform And
Neural Networks," in Proc. 2001 IEEE/EMBS 23rd Annual Conference,
pp. 1596 - 1599.
[9] H. S. Liu, T. Zhang and F. S. Yang, "A Multistage, Multimethod
Approach for Automatic Detection and Classification of Epileptiform
EEG," IEEE Transactions on Biomedical Engineering, vol. 49, pp. 1557
- 1566, 2002.
[10] P. Jahankhani, V. Kodogiannis, and K. Revett, "EEG Signal
Classification Using Wavelet Feature Extraction and Neural Networks,"
in Proc. 2003 International Symposium on Modern Computing, pp. 120
- 124.
[11] A. A. Mashakbeh, "Analysis Of Electroencephalogram To Detect
Epilepsy," Int. Journal Of Academic Research, vol. 2, pp. 63-69, 2010.
[12] M. M. Shaker, "EEG Waves Classifier using Wavelet Transform and
Fourier Transform," Int. Journal of Biological and Life Sciences, vol. 1,
pp. 85-90, 2005.
[13] I. Omerhodzic, E. Causevic, K. Dizdarevic, S. Avdakovic, M. Music, M.
Kusljugic, E. Hajdarpasic, N. Kadic, "First neurosurgical experience
with the wavelet based EEG in diagnostic of concussion," 11th Congress
of Neurosurgeons of Serbia, Nis, Serbia, 2008. Abstract book p. 18.
[14] L. M. Patnaik, O. K. Manyam, "Epileptic EEG detection using neural
networks and post-classification". Comput Methods Programs Biomed.
2008 Aug;91(2):100-9.
[15] A. Subasi, E. Erçelebi, "Classification of EEG signals using neural
network and logistic regression". Comput Methods Programs Biomed.
2005 May;78(2):87-99.
[16] K. Asaduzzaman, M. B. Reaz, F. Mohd-Yasin, K. S. Sim, M. S.
Hussain, "A study on discrete wavelet-based noise removal from EEG
signals", Adv Exp Med Biol. 2010;680:593-9.
[17] H. Adeli, Z. Zhou, N. Dadmehr. "Analysis of EEG records in an
epileptic patient using wavelet transform", J Neurosci Methods. 2003
Feb 15;123(1):69-87.
[18] L. Guo, D. Rivero, J. Dorado, J. R. Rabunal, A. Pazos, "Automatic
epileptic seizure detection in EEGs based on line length feature and
artificial neural networks", J Neurosci Methods. 2010 Aug
15;191(1):101-9.
[19] A. S. Zandi, M. Javidan, G. A. Dumont, R. Tafreshi, "Automated realtime
epileptic seizure detection in scalp EEG recordings using an
algorithm based on wavelet packet transform", IEEE Trans Biomed Eng.
2010 Jul;57(7):1639-51.
[20] P. Mirowski, D. Madhavan, Y. Lecun, R. Kuzniecky, Classification of
patterns of EEG synchronization for seizure prediction. Clin
Neurophysiol. 2009 Nov;120(11):1927-40.
[21] A. S. Zandi, G. A. Dumont, M. Javidan, R. Tafreshi, B. A. MacLeod, C.
R. Ries, E. A. Puil, "A novel wavelet-based index to detect epileptic
seizures using scalp EEG signals", Conf Proc IEEE Eng Med Biol Soc.
2008;2008:919-22.
[22] A. Subasi, A. Alkan, E. Koklukaya, M. K. Kiymik, "Wavelet neural
network classification of EEG signals by using AR model with MLE
preprocessing", Neural Netw. Sep 2005, vol. 18(7), 985-997.
[23] S. J. Schiff, A. Aldroubi, M. Unser, S. Sato, "Fast wavelet
transformation of EEG", Electroencephalogr Clin Neurophysiol. Dec
1994, vol. 91(6), 442-455.
[24] L. Senhadji, J. L. Dillenseger, F. Wendling, C. Rocha, A. Kinie,
"Wavelet analysis of EEG for three-dimensional mapping of epileptic
events", Ann Biomed Eng. Sep-Oct 1995, 23(5):543-52.
[25] C. E. D'Attellis, S. I. Isaacson, R. O. Sirne, "Detection of epileptic
events in electroencephalograms using wavelet analysis", Ann Biomed
Eng. Mar-Apr 1997, 25(2):286-93.
[26] H. Adeli, S. Ghosh-Dastidar, N. Dadmehr, "A wavelet-chaos
methodology for analysis of EEGs and EEG subbands to detect seizure
and epilepsy", IEEE Trans Biomed Eng. Feb 2007, 54(2):205-11.
[27] H. Leung, K. Schindler, A. Y. Chan, A. Y. Lau, K. L. Leung, E. H. Ng,
K. S. Wong, "Wavelet-denoising of electroencephalogram and the
absolute slope method: a new tool to improve electroencephalographic
localization and lateralization", Clin Neurophysiol. Jul 2009,
120(7):1273-81.
[28] I. Daubechies, Ten Lectures on Wavelets, Philadelphia: Society for
Industrial and Applied Mathematics, 1992.
[29] H. He and J. A. Starzyk, "A Self-Organizing Learning Array System for
Power Quality Classification Based on Wavelet Transform," IEEE
Transaction On Power Delivery, vol. 21(1), pp. 286-295, 2006.
[30] S. Mallat, A Wavelet Tour of Signal Processing, San Diego, CA:
Academic, 1998.
[31] S. Avdakovic and A. Nuhanovic, "Identifications and Monitoring of
Power System Dynamics Based on the PMUs and Wavelet Technique,"
International Journal of Energy and Power Engineering, vol. 3, pp. 202-
209, 2010.
[32] R. G. Andrzejak, K. Lehnertz, C. Rieke, F. Mormann, P. David, C. E.
Elger, "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, 64, 061907. [Online]
Available: http://epileptologie-bonn.de/cms/
[33] P. Settipalli, "Automated Classification Of Power Quality Disturbances
Using Signal Processing Technique and Neural Network", Ph.D.
dissertation, University of Kentucky; USA, 2007.
[34] S. Dreiseitl, L. Ohno-Machado, Logistic regression and artificial neural
network classification models: a methodology review, J. Biomed.
Inform. vol. 35, 352-359, 2002.
[35] I.A. Basheer, M. Hajmeer, Artificial neural networks: fundamentals,
computing, design, and application, J. Microbiol. Methods vol. 43, 3-31,
2000.
[36] B.B. Chaudhuri, U. Bhattacharya, Efficient training and improved
performance of multilayer perceptron in pattern classification,
Neurocomputing vol. 34, 11-27, 2000.
@article{"International Journal of Medical, Medicine and Health Sciences:51987", author = "I. Omerhodzic and S. Avdakovic and A. Nuhanovic and K. Dizdarevic", title = "Energy Distribution of EEG Signals: EEG Signal Wavelet-Neural Network Classifier", abstract = "In this paper, a wavelet-based neural network (WNN) classifier for recognizing EEG signals is implemented and tested under three sets EEG signals (healthy subjects, patients with epilepsy and patients with epileptic syndrome during the seizure). First, the Discrete Wavelet Transform (DWT) with the Multi-Resolution Analysis (MRA) is applied to decompose EEG signal at resolution levels of the components of the EEG signal (δ, θ, α, β and γ) and the Parseval-s theorem are employed to extract the percentage distribution of energy features of the EEG signal at different resolution levels. Second, the neural network (NN) classifies these extracted features to identify the EEGs type according to the percentage distribution of energy features. The performance of the proposed algorithm has been evaluated using in total 300 EEG signals. The results showed that the proposed classifier has the ability of recognizing and classifying EEG signals efficiently.
", keywords = "Epilepsy, EEG, Wavelet transform, Energydistribution, Neural Network, Classification.", volume = "4", number = "1", pages = "6-6", }
