# An Efficient Hybrid Model for Patient-Independent Seizure Prediction Using Deep Learning
## 1 Introduction  
Epilepsy is a brain disease that causes sudden recurring seizures, which are provoked by unusual electrical discharges in some brain cells [1; 2]. Such seizures can severely harm patients, as they might cause nervousness and injuries [3]. The World Health Organization (WHO) has stated that around 10% of the world population will have one seizure during their lifetime, which makes epilepsy one of the most common neurological disorders around the world [1; 2; 3; 4]. Hence, finding an efficient methodology for epileptic seizure prediction is considered a very important issue [5]. Studies on seizure prediction using electroencephalogram (EEG) signals has shown that EEG holds valuable physiological and pathological information that can significantly enhance the performance of seizure predictive models if properly analyzed [1; 2; 3; 4; 5; 6; 7]. In addition, the use of deep learning (DL) and neural network (NNs) techniques in the development of computer models for diseases detection, the identification of diseases, and many other domains, have attracted the interest of numerous researchers [8; 9; 10; 11; 12; 13]. For instance, the authors of [12] introduced a COVID-19 patient tracking system using Lagrange optimization and a distributed DL model to ensure that all data needed to track any suspected patient is received efficiently and reliably. To achieve a low square mean error, they deployed a one-dimensional convolution neural network (1D-CNN). Moreover, in real-world applications, such as that proposed in [13], a 1D-CNN deep learning model was used to calculate the optimum interference transmission power in communication between vehicles and everything. In addition, the authors of [14] showed the utility of DL for vehicle pollution detection using real-time images. Their outcome clearly demonstrated that a simple deep neural network (DNN) architecture is capable of producing significantenhancements in results. Likewise, the authors of [15] proposed a DL model based on a convolutional neural network (CNN) and spectrogram images from EEG signals in major depression detection. Meanwhile, in [16], to classify left-hand and right-hand movements from motor imagery-EEG signals, a classification model (MIDNN) based on the DNN model was developed. Using the Brain Computer Interface (BCI) and Emotiv datasets, their proposed model improved classification accuracy by 13.7 percent and 26.9 percent, respectively. The promising results achieved by computer models deploying DL and NNs in different domains have motivated other researchers to examine the effectiveness of using DL and NNs in analyzing epilepsy EEG signals. Consequently, extensive studies using different DL and NNs techniques applied to EEG signals have been undertaken in order to support epileptic seizure detection and prediction. In 2020, the authors of [17] introduced a spectrogram and 1D-CNN-based approach to detect epileptic seizures using EEG signals. Their design deployed a spectrogram for feature extraction (FE), which was then used as an input for the 1D-CNN for epileptic seizures detection. Using the Children's Hospital Boston and the Massachusetts Institute of Technology (CHB-MIT) datasets as a benchmark, their approach only achieved 77.57% accuracy in seizure detection [17]. Hence, the authors of [18] used sample entropy as a feature to feed to the Bi-LSTM model for seizure prediction using CHB-MIT dataset achieving an accuracy of 80.09% working on 13 patients. Later, in October 2020, the authors of [19] presented a binary single-dimensional convolutional neural network (BSDCNN) for seizure prediction that was tested using two benchmarks, the American Epilepsy Society Seizure Prediction Challenge (AES) dataset and the CHB-MIT. Their design diminished the parameter size by 7.2 times and the computation complexity by 25.5 times while achieving a precision loss of 2.74% [19].