Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions

By Rajeev Sharma, Ram Bilas Pachori. Expert systems with applications, 2015

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Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals (2015, methodology): “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 el”
Epileptic Seizures Detection Using Deep Learning Techniques: A Review (2020, background): “The EEG provides the voltage variations produced by the ionic current of neurons in the brain, which indicate the brain’s bioelectric activity [9].”
A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform (2017, methodology): “For the performance evaluation of the classifiers, we have computed three parameters [52], [53], called sensitivity (Sens = TP/(TP + FN)), specificity (Spec = TN/(TN + FP)), and accuracy (Acc = (TP + TN)/(TP + TN + FP + FN)), where TP (true positive) is the number of truly detected seizure epochs, F”