Influence of differential features in focal and non-focal EEG signal classification

Fasil, O. K.; Rajesh, R; Thasleema, T. M.

doi:10.1109/r10-htc.2017.8289042

Cited by 11 publications

(10 citation statements)

References 20 publications

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“…This is because RPS matrix needs optimum values of delay time τ and embedding dimension d, which is computed from the input signal by mutual information [10,11] and false nearest neighbor methods [10]. In [12], the influence of differential features were investigated in F and NF EEG signals classification indicating that differential features can play better performance in F signal detection. The second order difference plot (SODP) is a graphical representation of successive rates against each other and provides the data variability rate [13].…”

Section: Introductionmentioning

confidence: 99%

Fast and Accurate Classification F and NF EEG by Using SODP and EWT

Akbari¹,

Ghofrani²

2019

IJIGSP

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Removing the brain part, as the epilepsy source attack, is a surgery solution for those patients who have drug resistant epilepsy. So, the epilepsy localization area is an essential step before brain surgery. The Electroencephalogram (EEG) signals of these areas are different and called as focal (F) whereas the EEG signals of other normal areas are known as non-focal (NF). Visual inspection of multi-channels for F EEG detection is time-consuming along with human error. In this paper, an automatic and adaptive method is proposed based on second order difference plot (SODP) of EEG rhythms in empirical wavelet transform (EWT) domain as an adaptive signal decomposition. SODP provides the data variability rate or gives a 2D projection for rhythms. The feature vector is obtained using the central tendency measure (CTM). Finally, significant features, chosen by Kruskal-Wallis statistical test, are fed to K nearest neighbor (KNN) and support vector machine (SVM) classifiers. The achieved results of the proposed method in terms of three objective criteria are compared with state-of-the-art papers demonstrating an outstanding algorithm here in.

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Section: Introductionmentioning

confidence: 99%

Fast and Accurate Classification F and NF EEG by Using SODP and EWT

Akbari¹,

Ghofrani²

2019

IJIGSP

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“…In this work, seizure EEG signals and non-seizure EEG signals are classified by decomposing the EEG signal into IMFs using empirical mode decomposition. The frequencies beyond 60 Hz are irrelevant in the EEG analysis due to the nonavailability of proper information in higher frequencies [34]. A sixth order butterworth filter is used to remove frequencies beyond 60 Hz.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…To investigate the ability of each feature to classify seizures, experiments are conducted on all features individually. In classification task, the capacity of RBF kernel in support vector machine is already proved in various seizure studies [26,34]. In this work we have used RBF kernel in support vector machine for the classification task.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Empirical Mode Decomposition of EEG Signals for the Effectual Classification of Seizures

Ok¹,

Rajesh²

2020

Advances in Neural Signal Processing

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Empirical mode decomposition (EMD) is a remarkable method for the analysis of nonlinear and non-stationary data. EMD will breakdown the given signal into intrinsic mode functions (IMFs), which can represent natural signals effectively. In this work, the competence of EMD with traditional features to classify the seizure and non-seizure EEG signals is studied. Due to the complex nature of human brain, the EEG signals which are recorded from different regions of brain are nonstationary in nature. Different features such as entropy features (approximate entropy (ApEn), sample entropy (SmEn), Shannon entropy (ShEn), Rényi entropy (RnEn)), fractal dimension features (Petrosian fractal dimension, Higuchi fractal dimension, Katz fractal dimension), statistical features (mean, standard deviation and energy) and exponential energy features are extracted from IMFs and fed to a SVM classifier. The performances of extracted features are studied independently. The result shows that, the EMD method is well suited for complex seizure EEG signal classification.

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“…Each focal pair consists of one of the focal EEG channels for the x signal, and one of this channel's neighboring focal channels for the y signal, both simultaneously acquired from the same patient. The non-focal pairs were selected from nonfocal EEG channels in the same way [7,29]. All EEG signal were band-pass filtered by an fourth order Butterworth (0.5 Hz-150 Hz).…”

Section: Datasetmentioning

confidence: 99%

Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation

et al. 2020

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The epileptogenic focus is a brain area that may be surgically removed to control of epileptic seizures. Locating it is an essential and crucial step prior to the surgical treatment. However, given the difficulty of determining the localization of this brain region responsible of the initial seizure discharge, many works have proposed machine learning methods for the automatic classification of focal and non-focal electroencephalographic (EEG) signals. These works use automatic classification as an analysis tool for helping neurosurgeons to identify focal areas off-line, out of surgery, during the processing of the huge amount of information collected during several days of patient monitoring. In turn, this paper proposes an automatic classification procedure capable of assisting neurosurgeons online, during the resective epilepsy surgery, to refine the localization of the epileptogenic area to be resected, if they have doubts. This goal requires a real-time implementation with as low a computational cost as possible. For that reason, this work proposes both a feature set and a classifier model that minimizes the computational load while preserving the classification accuracy at 95.5%, a level similar to previous works. In addition, the classification procedure has been implemented on a FPGA device to determine its resource needs and throughput. Thus, it can be concluded that such a device can embed the whole classification process, from accepting raw signals to the delivery of the classification results in a cost-effective Xilinx Spartan-6 FPGA device. This real-time implementation begins providing results after a 5 s latency, and later, can deliver floating-point classification results at 3.5 Hz rate, using overlapped time-windows.

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Influence of differential features in focal and non-focal EEG signal classification

Cited by 11 publications

References 20 publications

Fast and Accurate Classification F and NF EEG by Using SODP and EWT

Fast and Accurate Classification F and NF EEG by Using SODP and EWT

Empirical Mode Decomposition of EEG Signals for the Effectual Classification of Seizures

Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation

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