## 1 Introduction
### Motivation and the Aim of the Article  
Efforts are continuously being made to develop effective and efficient detection methods that can be successfully applied in vagus nerve stimulation (VNS) systems [40]. Stimulators can be configured to automatically trigger therapy upon seizure detection [41]. Based on the analysis of iEEG signals, the stimulator can recognize characteristic seizure patterns and deliver the appropriate therapy, such as brain stimulation, to interrupt or mitigate the intensity of the seizure. Monitoring the frequency of seizures in a patient is also an important factor and can assist doctors in determining the appropriate therapy [42]. On the other hand, even experienced neurophysiologists who are skilled in interpreting iEEG recordings often have doubts about which signal fragments can be considered seizure-related. It is not uncommon for situations to arise where two neurophysiologists disagree on the identification of signal fragments associated with an epileptic seizure. It is expected that developing modern methods of processing and analyzing iEEG signals will help address this issue and identify signals that can be useful in diagnosis. Therefore, a crucial element is to compare multiple feature extraction methods and identify the best ones to interpret and understand the characteristics and morphology of seizure signals. Furthermore, the application of modern deep learning methods and the use of explainability techniques can pinpoint the signal elements that contribute the most to seizure detection.  
Considering that well-recorded and correctly labeled signals are best suited for feature comparison, the authors decided to utilize iEEG signals in their research. To enable the application of deep learning techniques, the signals in the database were divided into shorter windows, resulting in a large number of training and testing examples. Subsequently, typical machine learning techniques (including feature extraction and classification) were compared with deep learning techniques (CNN and LSTM). For the feature extraction task, multiple methods were employed, such as spectral analysis, autocorrelation, energy, chaos-related features, the attractor dimension, Lyapunov exponents, the correlation dimension of the attractor, Sevcik's fractal dimension, wavelet analysis, higher-order statistics, and empirical mode decomposition. The well-known and commonly used method of support vector machines was employed for the classification of signals with epileptic seizures. Our goal was to gather and comprehensively compare various artificial intelligence techniques, including both traditional machine learning methods and deep learning techniques, which are commonly used in the field of seizure detection. Then, by using evaluation measures such as accuracy, sensitivity, precision, and specificity, the potential usefulness of individual features was indicated. By employing the gradient-weighted class activation mapping (Grad-CAM) technique, signal fragments that contribute the most to the detection of epileptic seizures using CNN were identified.  
Figure 1 illustrates the schematic of the conducted research described in the article. Within these studies, a standard machine learning approach was applied, which included feature extraction, as well as a deep learning approach. In the context of this task, CNN and LSTM networks were used.