**Abstract**

Electroencephalography (EEG) signals are frequently used for the detection of epileptic seizures. 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. 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 study multichannel EEG signals collected from epilepsy patients are decomposed into IMFs, and then essential IMFs are selected. Finally, time- and spectral-domain, and nonlinear features are extracted from selected IMFs and classified. DMD is a new matrix decomposition method proposed as an iterative solution to problems in fluid flow analysis. We present single-channel, and multi-channel EEG based DMD approaches for the analysis of epileptic EEG signals. As a third method, we use the SST representations of seizure and pre-seizure EEG data. 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. Simulation results demonstrate that the proposed approaches achieve outstanding validation accuracy rates.

**Keywords:** epileptic EEG classification, empirical mode decomposition (EMD), dynamic mode decomposition (DMD), synchrosqueezing transform (SST), machine learning

### **1. Introduction**

Epilepsy, affecting approximately 4 and 10 per 1000 people of the world's population, is one of the most common acute neurological diseases. 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 [1, 2]. In order to detect or monitor epilepsy patients, long-term electroencephalogram (EEG) signals, which are records of the electrical activity generated by the brain, should be inspected visually by expert neurologists. However, this examination method is very time-consuming, bothersome, not efficient, and subjective process. Therefore, utilizing signal processing, machine learning, and artificial intelligence

methods for automatic seizure prediction and detection from epileptic EEG signals has become an active research field [2–5].

In the literature, seizure prediction and detection studies have been carried out using successful signal processing approaches in which many spectral, temporal, nonlinear, and statistical properties are calculated.

Automatic seizure detection and prediction studies have been conducted based on time-domain features such as energy, mean value, skewness, and kurtosis values [6–8], exponential energy [6] and, and frequency domain features such as Power spectral density features [9].

Also, entropy-based features such as fuzzy entropy (FuzzyEn), and sample entropy (SampEn) [10], sigmoid entropy [11], approximate entropy (ApEn) [12], weighted Permutation Entropy (WPE) [13], have also been commonly utilized to detect and predict epileptic seizures.

Additionally, in several epileptic seizure detections and prediction study, nonlinear features such as cross-bispectrum [4], fractal dimension, detrended fluctuation analysis (DFA), Hurst's exponent [3, 12] have been utilized and promising results have been provided.

On the other hand, various Time-Frequency (FT) analysis approaches have been also performed for epileptic seizure distinguish. The wavelet transform and its derivative [5, 14], Discrete WT (DWT) and Wavelet Packed Decomposition (WPD) [7] based approaches were successfully utilized in the seizure classification studies. Another TF analysis approaches such as The Hilbert Vibration Decomposition (HVD) [15], Variational Mode Decomposition (VMD), Hilbert transforms (HT) [16], the smoothed pseudo-Wigner-Ville distribution (SPWVD) [17], Hilbert–Huang transform (HHT) [18], short-time Fourier transform (STFT) [14, 19], the analytic time-frequency flexible wavelet transform (ATFFWT) [20], The Wigner–Ville distribution (WVD) [21] have been frequently used in seizure detection and prediction studies.

EMD [7, 8, 22] and its derivative approaches such as bivariate empirical mode decomposition (BEMD) [23], multivariate empirical Mode Decomposition (MEMD) [24], ensemble Empirical Mode Decomposition (EEMD) [25] that decompose a given signal into a limited number of zero-mean oscillations called Intrinsic Mode Functions (IMFs) have been developed for the analysis of nonlinear and non-stationary signals and have been successfully used in many seizure detection or prediction studies.

Generally, traditional Fourier-based methods such as CWT or STFT are not very effective in the TF analysis of non-stationary biosignals like EEG [26–28]. Successful seizure classification studies have been carried out using the Synchrosqueezing Transform (SST) method [28], which has been developed based on CWT and STFT [26–29], in order to achieve better TF representations (TFRs) in recent years.

The dynamic mode decomposition (DMD) and derivative approaches, a new matrix decomposition method, that introduced as a solution to problems encountered in fluid flow analysis by Schmidt [30], has recently been used to analyze epileptic EEG signals [31, 32].

In this chapter, three different advanced signal analysis methods are utilized for the classification of seizure and seizure-free EEG signals. The pre-seizure and seizure EEG segments were investigated using *(i)* EMD and its derivative EEMD methods, *(ii)* DMD method, and finally, *(iii)* SST and traditional STFT methods to achieve high classification performances. The rest of this chapter is organized as follows. In Section 2, EEG data set used in this study and employed signal analysis methods are described. Computer simulation results and discussion on the results of three different approaches are presented in Section 3. Conclusions of the study are drawn in Section 4.
