Identifying Epilepsy with Artificial Intelligence: An EEG Signal Processing Perspective

Around millions of people around the world suffer from epilepsy, which is a type of neurological disorder that causes seizures. Early identification is very important to ensure that proper treatment can be provided. Unfortunately, traditional methods, such as electrical stimulation of the brain, are not always accurate. The prevalence of epilepsy is a major cause of disability and death worldwide. Early diagnosis and treatment are very important for the development of effective and efficient therapy. Unfortunately, current methods can only provide inaccurate results. New methods for analyzing epilepsy using electroencephalography (EEG) signals have been developed due to the advances in AI, deep learning, and machine learning. These methods can analyze large amounts of data and identify features and patterns in the signals. The development of new technologies has led to the development of new and accurate methods for diagnosing and treating epilepsy, a chronic condition that affects millions of individuals. Unfortunately, currently, traditional methods such as electrical stimulation cannot provide the best results. Due to the emergence of new technologies, such as deep learning and artificial intelligence, the ability to identify epilepsy using an electroencephalography signal has been greatly improved. In this paper, we present a novel model that can be used to analyze and treat epilepsy using an AI-based approach. We evaluated the performance of this system using various state-of-the-art algorithms, such as the Naïve Bayes, random forest, support vector machine, and CNN. The proposed model is based on a combination of AI and signal processing techniques, which can analyze the signals and identify features and patterns that are indicative of epilepsy. It was evaluated against a large dataset of patients and healthy controls’ signals. The paper presents an AI-based model that can perform an efficient and accurate diagnosis of epilepsy using an EEG signal. Its findings are valuable for the development of new and more accurate methods for treating and identifying epilepsy. According to our results, the proposed model was able to achieve high sensitivity and accuracy in identifying epilepsy using EEG signals. In addition, the CNN algorithm performed well in the evaluation, demonstrating its potential to be used in clinical settings.