93.9 the ville phone number11/30/2022 ![]() ![]() EEG is the most frequently used technique for the diagnosis of epilepsy, prediction, detection, and classification of epileptic seizures owing to cost, safety, and easy applicability. ![]() Simulation results demonstrate that the proposed approaches achieve outstanding validation accuracy rates.Įpilepsy, affecting approximately 4 and people of the world’s population, is one of the most common acute neurological diseases. Various features are calculated and classified by Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Naive Bayes (NB), Logistic Regression (LR), Boosted Trees (BT), and Subspace kNN (S-kNN) to detect pre-seizure and seizure signals. As a third method, we use the SST representations of seizure and pre-seizure EEG data. We present single-channel, and multi-channel EEG based DMD approaches for the analysis of epileptic EEG signals. DMD is a new matrix decomposition method proposed as an iterative solution to problems in fluid flow analysis. Finally, time- and spectral-domain, and nonlinear features are extracted from selected IMFs and classified. In this study multichannel EEG signals collected from epilepsy patients are decomposed into IMFs, and then essential IMFs are selected. EMD and its derivative, EEMD are recently developed methods used to decompose nonstationary and nonlinear signals such as EEG into a finite number of oscillations called intrinsic mode functions (IMFs). In this chapter, advanced signal analysis methods such as Empirical Mode Decomposition (EMD), Ensembe (EMD), Dynamic mode decomposition (DMD), and Synchrosqueezing Transform (SST) are utilized to classify epileptic EEG signals. ![]() Electroencephalography (EEG) signals are frequently used for the detection of epileptic seizures. ![]()
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