# An Efficient Hybrid Model for Patient-Independent Seizure Prediction Using Deep Learning
## 1 Introduction
Furthermore, the authors of [20] proposed a one-dimensional capsule networks model for pre-ictal/inter-ictal perception, which reached a 97.74% classification accuracy using the F3-C3 channel pair. The experimental results showed that the 30 min prior to a seizure holds very valuable information that can support seizure prediction [20]. Recently, for implementing a patient-specific seizure prediction system, the authors of [21] designed an end-to-end framework that uses dilated convolutions with a multi-scale convolutional neural network. Their model reached an average proportion of time-in-warning of 6.3%, an average sensitivity of 93.3%, and an average false prediction rate of 0.007 per hour when tested on 16 patients from the CHB-MIT dataset [21]. Moreover, the authors of [22] designed a custom spiking neural network (SNN) to detect events of interest (EoI) in the 80-250 Hz ripple band and reject artifacts in the 500-900 Hz band. Meanwhile, the authors of [23] proposed a patient-specific prediction model. They converted raw signals into images and reached an accuracy of 94.3% when seizure prediction was done for five patients of the CHB-MIT dataset. However, the conversion of EEG signals to images could cause the loss of important information that CNNs could extract from raw signals. For the purpose of noise removal from EEG signals, discrete wavelet transform (DWT) has been widely used. The Daubechies sequence has good scalability and flexibility for the evaluation of boundary issues, according to [24]. In addition, Daubechies-4 (db4) and Daubechies-6 (db6) wavelets were used to eliminate noise from EEG signals in [9] and [10], respectively. In addition, in [25], it was proved that \"Symlet9\" was the most suitable wavelet transform for EEG signals to achieve wavelet de-noising through decomposition and reconstruction. Despite the fact that many researchers have addressed the issue of predicting epileptic seizures using DL learning techniques, the majority of these studies were patient-specific and relied on the historical data of a few patients to train their models. In addition, most previous research did not properly tackle the issue of the noisy channels of EEG signals. Being that they are scalp signals, EEG signals are challenging due to the presence of noise, which requires a pre-processing step before the prediction step. Consequently, this research focuses on determining if analyzing the historical data of all patients in the CHB-MIT dataset will support designing an efficient patient independent epileptic seizures prediction model. Additionally, a noise removal module is introduced using DWT techniques. The aim of this research is to design and implement an efficient hybrid model for the prediction of epileptic seizures using DL techniques. In addition, it aims to study the effect of using DWT techniques to introduce a noise removal module on the performance of the presented model. The contributions of this paper can be demonstrated as follows. A generalized non-patient-specific epileptic seizure prediction model is developed. The proposed model integrates DWT for EEG signal processing with DL techniques for efficient classification of EEG signals. Furthermore, it introduces a robust noise removal technique that is adaptable to different EEG signal channel selections. The rest of this paper is organized as follows. The next section describes the materials and methods used, including the dataset, explains the different modules used in the proposed approach and shows the 1-D CNN models' architectures. In Section 3 the experimental results using the two proposed DL models are presented. The analysis is then discussed in Section 4. Finally, Section 5 presents the conclusions.