Ictal EEG Classification based on State Space Modeling of Intrinsic Mode functions

Biju, K. S.; Jibukumar, M G

doi:10.1016/j.procs.2017.12.061

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…The use of EMD can be found for lung sound analysis in several cases, such as noise identification, velcro or crackle identification [22]- [24]. EMD can be used for EEG signal, several research has been done to do ictal EEG classification [19], [25], seizure classification [26], [27], artifact removal [28]- [30], and brain death analysis [31].…”

Section: Empirical Mode Decompositionmentioning

confidence: 99%

Epileptic Seizure Detection in EEG Signal using EMD and Entropy

Wijayanto¹,

Rizal²

2019

Journal of Electronic Systems

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Epilepsy is a disease caused by abnormal electrical activity in the brain. One of the techniques for diagnosing epilepsy is by analyzing electroencephalogram (EEG) signals. Various techniques were developed by researchers to analyze epileptic seizure on EEG signals. Because of the nonlinear, non-Gaussian, and nonstationary nature of EEG signals, methods such as empirical mode decomposition (EMD) are often used for analysis on EEG signals. The intrinsic mode function (IMF) of the EMD is believed to provide different information for normal EEG and seizure signals. Some features are taken from the IMF such as statistical features and spectral features. One of the differences between normal signals and abnormal signals is signal complexity where one of the metrics for measuring them is entropy. Several research used entropy combined with first and second order statistical features. In this study, only one entropy feature used to characterize each IMF produced from EMD for the classification of epileptic seizure EEG. Entropie used were Shannon entropy (ShEN), spectral entropy (SE), Renyi entropy (RE), and permutation entropy (PE). The highest accuracy produced by RE in eight IMF uses quadratic support vector machine (SVM) as the classifier. The accuracy of 97.3% with sensitivity of 97% and specificity of 99.75 % was achieved for classification in three data classes. The developed method is able to produce high accuracy with a relatively small number of features.

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Section: Empirical Mode Decompositionmentioning

confidence: 99%

Epileptic Seizure Detection in EEG Signal using EMD and Entropy

Wijayanto¹,

Rizal²

2019

Journal of Electronic Systems

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“…(4) EEG signals are, for example, weak, nonlinear, nonstationary, and sensitive in time; thus, timefrequency domain analysis and spatial filtering are widely used in feature extraction. (5) Timefrequency domain analysis mainly includes the short-term Fourier transform (STFT), (6) wavelet transform (WT), (7) wavelet package transform (WPT), (8) and common spatial pattern (CSP) method. (9) However, the time-frequency domain analysis methods based on STFT, WT, and WPT cannot achieve high resolution in the time and frequency domains.…”

Section: Introductionmentioning

confidence: 99%

Combination of Variational Mode Decomposition for Feature Extraction and Deep Belief Network for Feature Classification in Motor Imagery Electroencephalogram Recognition

Zhang¹,

Liang²,

Liu³

2021

Sensors and Materials

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Brain-computer interface (BCI) technology based on motor imagery (MI) can establish the connection between the brain and the outside world; this has gradually become an important application of human-machine hybrid intelligence enhancement, especially for medical rehabilitation treatment. Because of the nonlinear, nonstationary, and low signal-to-noise ratio (SNR) characteristics of electroencephalogram (EEG) signals, it is a great challenge to accurately classify MI-EEG signals. Toward this end, in this study, both variational mode decomposition (VMD) and a deep belief network (DBN) were applied to MI classification based on the dataset of a previous BCI competition. Firstly, the EEG signal was decomposed by VMD to obtain the narrow-band component. Then the marginal spectrum, the instantaneous energy spectrum under the characteristic frequency band, and time-frequency joint features were extracted by Hilbert transform to achieve feature fusion. Finally, the DBN was used to reduce the dimensions of high-dimensional features and recognize MI patterns. The experimental results show that the joint VMD feature extraction and DBN feature classification method avoids information omission caused by the manual optimization of the period and the frequency band of artificial determination imagery. On the other hand, the method of using VMD and DBN to automatically extract the characteristics of the optimal period and optimal frequency band can effectively improve the recognition rate of MI.

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Comparative Study on Epilepsy Prediction Using Modern Techniques

B.¹,

Biju²

2023

2023 International Conference on Control, Communication and Computing (ICCC)

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Ictal EEG Classification based on State Space Modeling of Intrinsic Mode functions

Cited by 4 publications

References 13 publications

Epileptic Seizure Detection in EEG Signal using EMD and Entropy

Epileptic Seizure Detection in EEG Signal using EMD and Entropy

Combination of Variational Mode Decomposition for Feature Extraction and Deep Belief Network for Feature Classification in Motor Imagery Electroencephalogram Recognition

Comparative Study on Epilepsy Prediction Using Modern Techniques

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