Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions

Pachori, Ram Bilas; Patidar, Shivnarayan

doi:10.1016/j.cmpb.2013.11.014

Cited by 240 publications

(102 citation statements)

References 36 publications

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“…The classification performance of the LS-SVM based classification of heart rate signals of CAD and normal subjects can be analyzed by computing the following parameters [61][62][63]: sensitivity (Sen), specificity (Spec), accuracy (Acc) and Matthews correlation coefficient (Mcc). Sensitivity determines the probability of actual positives which are correctly produced as such when used on the CAD affected population.…”

Section: Classification Performance Measuresmentioning

confidence: 99%

Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals

Patidar

Pachori

Acharya

2015

Knowledge-Based Systems

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156

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Section: Classification Performance Measuresmentioning

confidence: 99%

Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals

Patidar

Pachori

Acharya

2015

Knowledge-Based Systems

Self Cite

156

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“…Recently, many empirical mode decomposition (EMD) [30]-based techniques have been developed for analysis and classification of EEG signals [31][32][33][34][35][36][37][38]. The IMFs of EEG signals are symmetric, amplitude and frequency modulated (AM-FM) components.…”

Section: Introductionmentioning

confidence: 99%

“…Many features are extracted from the IMFs of EEG signals to study the pathological states of the brain. These features are the mean frequency of IMFs computed from the Fourier-Bessel series expansion [31], the area computed from the analytic signal representation (ASR) of the IMFs [32,33], the 95% confidence ellipse area of the second-order difference plot (SODP) of IMFs [33,34], the 95% confidence ellipse area and interquartile range (IQR) of the Euclidean distances parameters extracted from the 2D and 3D phase space representation (PSR) of IMFs [35], the histogram-based features extracted from time-frequency images obtained using the Hilbert-Huang transform [36], multi-level local patterns [37], the coefficient of variation and the fluctuation index computed from IMFs [38], etc.…”

Section: Introductionmentioning

confidence: 99%

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

Sharma

Pachori

Acharya

2015

Entropy

284

175

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The brain is a complex structure made up of interconnected neurons, and its electrical activities can be evaluated using electroencephalogram (EEG) signals. The characteristics of the brain area affected by partial epilepsy can be studied using focal and non-focal EEG signals. In this work, a method for the classification of focal and non-focal EEG signals is presented using entropy measures. These entropy measures can be useful in assessing the nonlinear interrelation and complexity of focal and non-focal EEG signals. These EEG signals are first decomposed using the empirical mode decomposition (EMD) method to extract intrinsic mode functions (IMFs). The entropy features, namely, average Shannon entropy (ShEn Avg ), average Renyi's entropy (RenEn Avg ), average approximate entropy (ApEn Avg ), average sample entropy (SpEn Avg ) and average phase entropies (S1 Avg and S2 Avg ), are computed from different IMFs of focal and non-focal EEG signals. These entropies are used as the input feature set for the least squares support vector machine (LS-SVM) classifier to classify into focal and non-focal EEG signals. Experimental results show that our proposed method is able to differentiate the focal and non-focal EEG signals with an average classification accuracy of 87% correct.

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“…Patidar [21] classified normal and seizure EEG using empirical mode decomposition (EMD) and second-order difference plot (SODP) features with ANN model on Andrzejak Dataset. Their study combines EMD and SODP methods for feature extraction.…”

Section: Experimental Results and Conclusionmentioning

confidence: 99%

Epileptic State Detection: Pre-ictal, Inter-ictal, Ictal

Yayık

Yıldırım²,

Kutlu³

et al. 2015

Int J Intell Syst Appl Eng

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Epileptic seizure detection and prediction from electroencephalography (EEG) is a vital area of research. In this study, SecondOrder Difference Plot (SODP) is used to extract features based on consecutive difference of time domain values from three states of EEG (pre-ictal, ictal and inter-ictal), and Multi-Layer Neural Network classifier is used to classify these three classes. The proposed technique is tested on a publicly available EEG database and classified with Naive Bayes and k-nearest neighbor classifiers. As a result, it is shown that overall accuracy of 98.70% can be achieved by using the proposed system with Neural Network classifier.

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Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions

Cited by 240 publications

References 36 publications

Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals

Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

Epileptic State Detection: Pre-ictal, Inter-ictal, Ictal

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