# A Hybrid DenseNet-LSTM Model for Epileptic Seizure Prediction
## 2 Related Work  
Over the past few years, research in the field of seizure prediction has been ongoing. The basic assumption of seizure prediction is that there is a difference between the interictal and preictal states. In early seizure prediction studies, threshold-based methodology [7; 8; 9; 10; 11] or machine learning techniques such as Support Vector Machines (SVM) [12; 13; 14] were used a lot, but recently, deep learning methods [15; 16; 17] such as CNN have been studied a lot. [PERSON]. [18] was the first to propose training a deep learning classifier to identify seizures in EEG images, similar to how clinicians identify seizures through visual inspection. Ref. [19] proposed a method of extracting the univariate spectral power of intracranial EEG signals, classifying them through SVM, and removing sporadic and incorrect information  
Figure 1: An example of epileptic brain states which contains an interictal part, preictal part, ictal part and postictal part.The horizontal axis displays the time and the vertical axis displays the measured voltage.  
using Kalman filters. Their methodology consisted of 80 seizures and 18 patients on the Freiburg dataset, reaching 98.3% sensitivity and 0.29 false positive rate (FPR). Ref. [20] proposed a method of extracting the power spectral density ratio of the EEG signal, further processing it by a second-order Kalman filter, and then inputting it into the SVM classifier for classification. The dataset used for the evaluation is the same as the previous data, reaching 100% sensitivity and 0.03 FPR. Ref. [21] proposed a mechanism for calculating the phase-locking values between the scalp EEG signals and classifying them into interictal and preictal states through SVM using this. Their proposed method was applied to the CHB-MIT dataset consisting of 21 patients and 65 seizures, reaching a sensitivity of 82.44% and a specificity of 82.76%.  
In seizure prediction studies using deep learning algorithms, CNN is attracting the most attention. Since seizure prediction studies using CNN usually require data in the form of images as input, the EEG signal is converted into a two-dimensional form through a preprocessing method. The authors of [22] proposed a method of dividing the raw EEG signal by a window size of 30 s, applying Short-Time Fourier Transform (STFT) to extract spectrum information, and then using it as an input to CNN. In the experiment using 64 seizures from 13 patients in the CHB-MIT dataset, reaching a sensitivity of 81.2% and an FPR of 0.16. In [23], an image is transformed into a time-frequency form using Continuous Wavelet Transform (CWT) to see the various frequency bands of EEG. The authors proposed a method of predicting seizures by learning the difference between interictal and preictal states using the transformed data as an input to CNN. The same dataset as before was used, and as a result of testing 18 seizures from 15 patients, the average FPR was 0.142 and was unpredictable for three seizures. In [24], seizure prediction using preprocessed features with spectral band power, statistical moment, and Hjorth parameters as inputs to a multi-frame 3D CNN model is performed, achieving a sensitivity of 85.71% and FPR of 0.096 in the CHB-MIT dataset.