# Classification of EEG Signals for Prediction of Epileptic Seizures
## 3 Overview of Proposed Method  
We propose a patient-specific method for seizure prediction that predicts a seizure by detecting the start of preictal state. A flowchart for the proposed method is shown in Figure 2. The dataset used in this study is the widely used, publicly available CHB-MIT scalp EEG dataset [75]. It has recordings of 22 subjects with 23-channel signal recording and a sampling frequency of 256 Hz. PREP pipeline [76] is used to remove the powerline noise.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline
**Method** & **Preprocessing** & **Features** & **Classifier** \\ \hline
[36] & Bandpass Filter & Zero Crossings & Variational GMM \\ \hline
[37] & - & Codebooks Construction, Bag of Waves Segmentation & ELM \\ \hline
[38] & Fast Fourier Transform & Spectral Features & Threshold for States \\ \hline
[39] & Short-Time Fourier Transform & Convolutional Neural Networks & Convolutional Neural Networks \\ \hline
[41] & Discrete Wavelet Transform & Convolutions Neural Network & Convolutional Neural Networks \\ \hline
[42] & Bandpass Filter & Lyapunov Exponent, Fourier Transform & Neural Networks \\ \hline
[43] & Derivatives and Statistical Moments & Derivative, Local Variance, Median Filtering & Thresholding \\ \hline
[44] & - & Statistical features using CSP & Linear Discriminant Analysis \\ \hline
[45] & Empirical Mode Decomposition & Phase Locking Value & Support Vector Machines \\ \hline
[46] & Standard Deviation/Bandpass Filter & Phase Locking Value & Thresholding \\ \hline
[74] & - & Hilbert Transform & Least Squares—Support Vector Machine \\ \hline \end{tabular}
\end{table}
Table 1: Preprocessing, feature extraction, and classifiers in existing methods.  
After noise is removed from the dataset, a sliding window of 30 s with 50% overlap is selected, and short time Fourier transform (STFT) is applied in order to further enhance the SNR and to convert from the time to frequency domain. The overlapping window is selected only in the preictal data. This is done to overcome the class imbalance between the interictal and preictal class. As stated earlier, the interictal state is the normal state of the brain, and the preictal state is recorded a few minutes before the occurrence of a seizure, so there is an inherent imbalance between the amount of data available for the two states. An overlapping window is used to perform oversampling of the preictal state. This is done across the board to all the channels and for each occurrence of preictal state. The interictal data is converted to frequency domain using a 30 s non-overlapping window. Figure 3 shows a visual representation of the overlapping and non-overlapping windows. Statistical moments have been extracted as handcrafted features, and CNN is applied for feature extraction. A custom, three-layer CNN is used that takes a \(65\times 117\times 23\) matrix as the input size. Batch normalization is used to minimize internal covariate shift. Dense layers of the CNN architecture are removed. A feature vector containing both handcrafted and automated features has been created and given to the LSTM for classification.  
Figure 3: Non-overlapping/overlapping window selection.  
Figure 2: Flow diagram of the proposed epilepsy prediction system.