Analysis of non-seizure and seizure activity using intracranial EEG signals and empirical mode decomposition based approximate entropy

Ramakrishnan, Jayabrabu; Kanagaraj, Bommanna Raja

doi:10.4066/biomedicalresearch.29-16-2323

Cited by 9 publications

(8 citation statements)

References 21 publications

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“…Additionally, high-and low-frequency components can be analyzed individually by application of Wavelet Packet Decomposition (WPD), decomposing obtained signal into two components (Alickovic, Kevric, & Subasi, 2018). Alongside CWT and DWT, Empirical Mode Decomposition (EMD) as well as Hilbert-Huang Transform (HHT) are applied to nonstationary, non-linear EEG signal (Mutlu, 2018;Krishnan & Samiappan, 2018;Ramakrishnan & Kanagaraj, 2018;Das & Bhuiyan, 2016). Among numerous practical application of EMD on EEG signal, the inceptive application is found in removal of noise as well as some artifacts from EEG signal (Das & Bhuiyan, 2016).…”

Section: Identification Of Eeg Featuresmentioning

confidence: 99%

“…Revealed intrinsic modes of oscillations are closely related to instantaneous frequency; precisely, localized frequency within narrow frequency band (Krishnan & Samiappan, 2018). Statistical analysis of EMD related signatures is often applied by means of particular dysfunction diagnose (for instance: epileptic seizures) (Ramakrishnan & Kanagaraj, 2018;Das & Bhuiyan, 2016). Hilbert-Huang Transform is commonly discussed in terms of EMD extension as it uses IMFs to obtain Hilbert spectrum.…”

Section: Identification Of Eeg Featuresmentioning

confidence: 99%

“…Hilbert spectrum is three-dimensional representation of the amplitude, the time, and instantaneous frequency of the signal. According to (Mutlu, 2018;Krishnan & Samiappan, 2018;Ramakrishnan & Kanagaraj, 2018), the Hilbert-Huang spectral analysis and its components such as marginal spectrum, mean marginal spectrum, degree of statistic stationarity, and similar are utilized for the detection of particular dysfunctions and abnormal EEG signatures.…”

Section: Identification Of Eeg Featuresmentioning

confidence: 99%

“…To provide more detailed representation of information conveyed by EEG signal, a number of nonlinear techniques are utilized: Higher Order Spectra, Correlation and Fractal Di- mension, Approximate Entropy, Hurst Exponent, as well as Recurrence Quantification Analysis. All these techniques are applicable to an inspection of nonlinear non-stationary signal, relying on signal frequency spectrum whilst revealing particular relations between harmonic signal components (crossand auto-correlations, the second/third order correlation and its variations) (Krishnan & Samiappan, 2018;Ramakrishnan & Kanagaraj, 2018). To select a subset of features to be used for classification or pattern recognition purposes, an automated procedures and algorithms are introduced.…”

Section: Identification Of Eeg Featuresmentioning

confidence: 99%

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Identification of Diagnostic-related Features Applicable to EEG Signal Analysis

2018

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The regulation of functions such as respiratory or heart rate in human body as well as the control of motor movements are under the control of nervous system. As these actions and correlated tasks are directly influenced by the brain, the brain monitoring gives the possibility to differentiate the tasks, enabling at the same time the prediction of further actions. In this contribution, publicly available electroencephalography (EEG) datasets are analyzed with respect to the detection of epileptic seizure occurrence and BCI-related actions (here: cued motor imagery). For these purposes, timefrequency- based feature extraction alongside different classification methods is used. To perform the classification, Artificial Neural Network (ANN) and Support Vector Machine (SVM) are utilized and compared with previously obtained results. The feasibility of particular features for the detection of epileptic seizures and BCI-related tasks is discussed. Four different feature vectors per analyzed problem are identified. Acceptable accuracy of classification using ANN- and SVMbased classifiers is achieved using identified feature vectors.

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Section: Identification Of Eeg Featuresmentioning

confidence: 99%

Section: Identification Of Eeg Featuresmentioning

confidence: 99%

Section: Identification Of Eeg Featuresmentioning

confidence: 99%

Section: Identification Of Eeg Featuresmentioning

confidence: 99%

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Identification of Diagnostic-related Features Applicable to EEG Signal Analysis

2018

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“…Recently, seizure prediction was extremely investigated through different models of automatic EEG signals classification and seizure detection [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. These models generally include three steps and widely vary in their theoretical approaches of the problem, validation of results and the amount of data analyzed.…”

Section: Introductionmentioning

confidence: 99%

EEG Signal Classification using LDA and MLP Classifier

Atangana¹,

Tchiotsop²,

Kenné³

et al. 2020

HIIJ

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Electroencephalography (EEG) is the recording of electrical activities along the scalp. EEG measures voltage fluctuations resulting from ionic current flows within the neurons of the brain. Diagnostic applications generally focus on the spectral content of EEG, which is the type of neural oscillations that can be observed in EEG signal. EEG is most often used to diagnose epilepsy, which causes obvious abnormalities in EEG readings. This powerful property confirms the rich potential for EEG analysis and motivates the need for advanced signal processing techniques to aid clinicians in their interpretations. This paper describes the application of Wavelet Transform (WT) for the processing of Electroencephalogram (EEG) signals. Furthermore, the linear discriminant analysis (LDA) is applied for feature selection and dimensionality reduction where the informative and discriminative two-dimension features are used as a benchmark for classification purposes through a Multi-Layers Perceptron (MLP) neural network. For five classification problems, the proposed model achieves a high sensitivity, specificity and accuracy of 100%.Finally, the comparison of the results obtained with the proposed methods and those obtained with previous literature methods shows the superiority of our approach for EEG signals classification and automated diagnosis.

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