Robust expert system design for automated detection of epileptic seizures using SVM classifier

Swami, Piyush; Godiyal, Anoop Kant; Santhosh, Jayasree; Panigrahi, B. K.; Bhatia, M. P. S.; Anand, Sneh

doi:10.1109/pdgc.2014.7030745

Cited by 12 publications

(11 citation statements)

References 24 publications

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“…ApEn values of the wavelet coefficients of all the 31 nodes of the decomposition tree were used as a feature vector, while a genetic algorithm was employed to reduce the number of features and find the optimal feature subset that maximizes the classification performance of a learning vector quantization (LVQ) scheme. Swami et al [16] used wavelet packet decomposition to extract valuable information from the EEG signal. A six-level wavelet packet decomposition yielding 64 nodes was performed, and several statistical features were extracted from each node.…”

Section: Wpd-based Studiesmentioning

confidence: 99%

“…Most of the studies for epileptic activity detection/classification using EEG signal processing, formulate methodologies that analyse the EEG signal by extracting informative features from it [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. To this end, spectral analysis of the EEG signal is essential, since epileptic activity interrupts normal brain functionality.…”

Section: Introductionmentioning

confidence: 99%

“…The EEG spectral analysis is based on a set of frequency sub-bands. Researchers have mainly used wavelet transform (WT) [3][4][5][6][7][8][9][10][11][12][13][14][15][16] and time-frequency distributions (TFD) [17][18][19][20] to analyse the EEG spectral patterns. However, although spectral analysis is a well-known approach, with numerus studies including spectral characteristics in the features extracted from the EEG, the importance of the frequency sub-bands that are used to analyse the signal has never been thoroughly investigated in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…However, although spectral analysis is a well-known approach, with numerus studies including spectral characteristics in the features extracted from the EEG, the importance of the frequency sub-bands that are used to analyse the signal has never been thoroughly investigated in the literature. It is medically established that brainwaves are divided based on their frequency into several sub-bands, being delta (1-4 Hz), theta (4)(5)(6)(7)(8), alpha (8)(9)(10)(11)(12)(13), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma [21]. Thus, several researchers roughly focus on these sub-bands [3-14, 17, 18], with the technical limitations that the analysis technique imposes (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…WT). Thus, the importance of the frequency sub-bands and their limits have not been analysed in the literature, since in WT-based approaches the frequency sub-bands are automatically set [3][4][5][6][7][8][9][10][11][12][13][14][15][16], while in TFD-based methodologies, an attempt to compare the impact of different sub-bands has been presented [17], however not being a systematic approach since only four different sub-band combinations were analysed.…”

Section: Introductionmentioning

confidence: 99%

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Spectral information of EEG signals with respect to epilepsy classification

Tsipouras

2019

EURASIP J. Adv. Signal Process.

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Background: The spectral information of the EEG signal with respect to epilepsy is examined in this study. Method: In order to assess the impact of the alternative definitions of the frequency sub-bands that are analysed, a number of spectral thresholds are defined and the respective frequency sub-band combinations are generated. For each of these frequency sub-band combination, the EEG signal is analysed and a vector of spectral characteristics is defined. Based on this feature vector, a classification schema is used to measure the appropriateness of the specific frequency sub-band combination, in terms of epileptic EEG classification accuracy. Results: The obtained results indicate that additional frequency band analysis is beneficial towards epilepsy detection. Conclusions: This work includes the first systematic assessment of the impact of the frequency sub-bands to the epileptic EEG classification accuracy, and the obtained results revealed several frequency sub-band combinations that achieve high classification accuracy and have never been reported in the literature before.

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Section: Wpd-based Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spectral information of EEG signals with respect to epilepsy classification

Tsipouras

2019

EURASIP J. Adv. Signal Process.

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Penalized Functional Connectivity Maps for Patients With Focal Epilepsy

et al. 2021

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Objective: This study demonstrates how functional connectivity (FC) patterns are affected in direct relation to the lobe that is mostly affected by seizures. Methods: The novel idea of penalized FC (pFC) maps is compared against standard FC maps in the four fundamental EEG frequency sub-bands. The FC measure between any two specific electrodes is scaled depending on the probability of true FC between them, and their power content with respect to the two electrodes of maximum power within each frequency sub-band. The algorithm is automated and introduces adaptive power penalization based on the power distribution of the different sub-bands. Results: The pFC maps were found to be more effective at suppressing the local connectivity in the lobes that are less affected by the interictal epileptiform discharges (IEDs). More precisely, given the least amount of power penalization, pFC maps of the theta sub-band reveal statistical significance in terms of increased local connectivity margin of the affected region as compared to the standard FC maps. However, they cannot be solely relied upon as other sub-bands could alternatively show high local connectivity across different patients within the region of interest. Conclusion: penalized functional connectivity maps intrinsically provide more information regarding the whole brain network in context to regions of interest where the active lobe is determined by the neurologists to contain the focal source. Significance: Findings suggest that (1) the significant sub-band varies from patient to patient while remaining relatively consistent within the IED segments of a same patient, and (2) the pFC maps have an advanced capability in terms of pinpointing to a region of interest of the active lobe, and as such can play a critical role in providing insight as to a region of interest where the 3D source might be located when solving the ill-posed inverse problem.INDEX TERMS Focal epilepsy, Interictal Epileptiform Discharges (IEDs), penalized Functional Connectivity (pFC), penalized Weighted Phase Lag Index (pWPLI).

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