## 4 Results and Discussion
In the case of our research material, the signals were recorded from the surface of the brain, and it should be noted that they contain significantly more diagnostic information than EEG signals recorded from the scalp. However, in practice, this entails a substantial increase in the costs of the recording itself and significant involvement of medical personnel. Although the number of examples is considerable, they represent recordings from only five patients. Therefore, the recorded signals do not represent all possible signals recorded for a much larger population of individuals. This strongly calls into question the direct translation of sensitivity, precision, specificity, and accuracy measures to the broader population. It should be remembered that there are many factors causing epilepsy, and recorded EEG signals may vary. Furthermore, the recorded signals cover only a narrow time window, as we do not know the duration of the recording or the basis for the selection of the recordings. We do not have complete information on how the recordings were identified as either seizure or non-seizure events. We lack information on the criteria used by experts to assign a signal fragment as a seizure or non-seizure. It should be noted that neurophysiologists often do not fully agree in this regard. Therefore, the problem of seizure detection should be approached not only through the lens of evaluation coefficients such as accuracy, precision, sensitivity, and specificity but also within the broader context of data collection and organization. It should be noted that although EEG signals may seem simpler to acquire, they come with certain difficulties. As mentioned earlier, they are often heavily influenced by physiological and technical artifacts. Additionally, there is the challenge of selecting the appropriate EEG channels that can capture changes related to the characteristic features of seizures. Each patient, in fact, has epilepsy foci located in different regions. For each patient, there may be different morphological changes in the EEG signal indicative of a seizure. Therefore,seizure detection using EEG signals appears to be a considerably more challenging problem, and one must approach the published results critically in the literature. Deep learning methods, including CNN and LSTM networks, are powerful tools for medical analysis and diagnosing various diseases. However, their operation is often difficult to comprehend for humans because these networks learn from vast amounts of data and complex patterns. Doctors want to understand why a network made a specific diagnosis or decision in order to trust the results better. When analyzing medical outcomes, there is a need to confirm whether the CNN network is interpreting the data correctly. Doctors want to ensure that the network recognizes important features and pathologies and takes relevant information into account when making decisions. Examining the functioning of CNN networks allows the verification of whether the network aligns with medical knowledge. Analyzing the performance of CNN networks can help doctors identify which signal or image features are relevant for diagnosing a specific disease. By analyzing the weights and activations of specific neurons in the network, doctors can understand which areas are particularly important for diagnostic decision-making. CNN networks can detect subtle patterns or dependencies in data that may escape the human eye. Doctors can gain new knowledge by uncovering these patterns. Based on the analysis of CNN network performance, doctors can suggest improvements or modifications to the diagnostic process. The gradient-weighted class activation mapping (Grad-CAM) is an interpretability method used to gain insights into the decision-making process of a deep neural network [129]. Grad-CAM, an extension of the class activation mapping (CAM) technique, assesses the significance of individual neurons in the network's predictions by examining the gradients of the target class propagated through the network.