Abstract
Various methods have been developed by researchers to recognize brain abnormalities through EEG signals. One of the diseases or disorders of the brain is seizures in epilepsy. EEG signals in seizure conditions display a different pattern compared to EEG signals in normal conditions. Researchers analyzed the EEG signal using a variety of observed approaches. One phenomenon used to analyze EEG signals is signal complexity. Signal complexity captures fluctuating patterns of EEG signals quantizing them to distinguish normal and seizure signal conditions. In this study, we propose the proper feature extraction method based on the basic characteristic of the signal. We extract the EEG signal’s information using entropy calculation from the intrinsic mode function (IMF entropy). Our main goal is to distinguish normal and seizure EEG signals. The entropy is calculated from the IMF resulted from empirical mode decomposition (EMD), then entropy from the relative energy of each IMF. To test the performance of the proposed feature extraction method, the support vector machine (SVM) is used as a classifier. The highest accuracy is 86.3%, sensitivity is 86.33%, and the specificity is 93.17% for three data classes: normal, interictal, and seizure. The proposed method has the potential to improve its performance, considering there are still many variations of EMD methods and decomposition levels that can be evaluated. Furthermore, testing on more massive datasets is interesting to do in future research.
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References
Yuvaraj R, Murugappan M, Mohamed Ibrahim N, Sundaraj K, Omar MI, Mohamad K, Palaniappan R (2014) Detection of emotions in Parkinson’s disease using higher order spectral features from brain’s electrical activity. Biomed Signal Process Control 14:108–116
Malar E, Gauthaam M (2020) Wavelet analysis of EEG for the identification of alcoholics using probabilistic classifiers and neural networks. Int J Intell Sustain Comput 1:3
Djemili R, Bourouba H, Amara Korba MC (2016) Application of empirical mode decomposition and artificial neural network for the classification of normal and epileptic EEG signals. Biocybern Biomed Eng 36:285–291
Patil A, Deshmukh C, Panat AR (2016) Feature extraction of EEG for emotion recognition using Hjorth features and higher order crossings. In: IEEE on 2016 Conference on advances in signal processing (CASP), pp 429–434
Wijayanto I, Rizal A, Hadiyoso S (2018) Multilevel wavelet packet entropy and support vector machine for epileptic EEG classification. In: Proceedings—2018 4th international conference on science and technology, ICST 2018
Ferenets R, Lipping T, Anier A, Jantti V, Melto S, Hovilehto S (2006) Comparison of entropy and complexity measures for the assessment of depth of sedation. IEEE Trans Biomed Eng 53:1067–1077
Padma Shri TK, Sriraam N (2016) Spectral entropy feature subset selection using SEPCOR to detect alcoholic impact on gamma sub band visual event related potentials of multichannel electroencephalograms (EEG). Appl Soft Comput 46:441–451
Shaikh, MHN, Farooq O, Chandel G (2019) EMD analysis of EEG signals for seizure detection. In: Lecture notes in electrical engineering, pp 189–196
Tripathi D, Agrawal N (2019) Epileptic seizure detection using empirical mode decomposition based fuzzy entropy and support vector machine. In: Lecture notes in electrical engineering, pp 109–118
Li S, Zhou W, Yuan Q, Geng S, Cai D (2013) Feature extraction and recognition of ictal EEG using EMD and SVM. Comput Biol Med 43:807–816
Wijayanto I (2019) Epileptic seizure detection in EEG signal using EMD and entropy. In: The international conference on advancement in data science, E-learning and information systems 2019 ICADEIS2019)
Yu Y, YuDejie Junsheng C (2006) A roller bearing fault diagnosis method based on EMD energy entropy and ANN. J Sound Vib 294:269–277
Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE (2001) 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
Wijayanto I, Hartanto R, Nugroho HA (2020) Comparison of empirical mode decomposition and coarse-grained procedure for detecting pre-ictal and ictal condition in electroencephalography signal. Inf Med Unlocked 19:100325
Sharma R, Pachori R, Acharya U (2015) Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals. Entropy 17:669–691
Muralidhar Bairy, G., Hagiwara, Y.: Empirical Mode Decomposition-Based Processing For Automated Detection Of Epilepsy. J. Mech. Med. Biol. 19, (2019)
Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc London Ser A Math Phys Eng Sci 454:903–995
Xue Y, Cao J, Du H, Zhang G, Yao Y (2016) Does mode mixing matter in EMD-based highlight volume methods for hydrocarbon detection? Experimental evidence. J Appl Geophys 132:193–210
Tao R, Ren H, Peng X (2017) Modeling, design and simulation of systems. Asian Simul Conf 752:249–260
Mohammadpour M, Hashemi SMR, Houshmand N (2017) Classification of EEG-based emotion for BCI applications. In: IEEE on 2017 Artificial intelligence and robotics (IRANOPEN), pp 127–131
Bradbury JH, Jenkins GA (1984) Determination of the structures of trisaccharides by 13C-n.m.r. spectroscopy. Carbohydr Res 126:125–156
Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on Computational learning theory—COLT ’92, pp 144–152. ACM Press, New York, USA
Cevikalp H (2017) Best fitting hyperplanes for classification. IEEE Trans Pattern Anal Mach Intell 39:1076–1088
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Rizal, A., Wijayanto, I., Hadiyoso, S. (2021). Seizure Classification on Epileptic EEG Using IMF-Entropy and Support Vector Machine. In: Triwiyanto, Nugroho, H.A., Rizal, A., Caesarendra, W. (eds) Proceedings of the 1st International Conference on Electronics, Biomedical Engineering, and Health Informatics. Lecture Notes in Electrical Engineering, vol 746. Springer, Singapore. https://doi.org/10.1007/978-981-33-6926-9_33
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