Evaluating Ai for Epilepsy Detection: Automated Recognition of Characteristic Eeg Patterns
Year: 2026
Type: article
Abstract: Electroencephalography (EEG) is a crucial diagnostic tool in epilepsy, providing high temporal resolution for detecting epileptic seizures and interictal discharges. Despite its significance, EEG analysis faces challenges such as susceptibility to artifacts, individual variability, and the need for specialized expertise. In recent years, the rapid and intensive development of artificial intelligence (AI), in particular machine learning and deep learning methods, has shown that their application in EEG analysis can be useful in everyday clinical practice. The integration of artificial intelligence offers promising solutions to enhance EEG interpretation, automate routine analysis, and improve diagnostic accuracy. Machine learning algorithms, particularly convolutional neural networks, have demonstrated expertise-level performance in classifying EEG signals and predicting seizures, reducing clinician workload and increasing efficiency. AI applications extend to wearable devices and implantable systems for continuous seizure monitoring and personalized therapy. However, challenges remain, including data quality, noise interference, privacy concerns, and the translation of research models into clinical practice. Future developments should focus on refining AI systems, improving dataset quality, and ensuring accessibility to enhance the diagnosis and management of epilepsy. Methodology: For the purpose of this paper, electronic databases such as Scopus, Google Scholar, and PubMed were searched. The literature search covered publications released primarily between 2016 and 2025, with particular emphasis on studies published within the last decade, reflecting the dynamic development of artificial intelligence methods in EEG analysis. A literature review was conducted using the following keywords: electroencephalography (EEG), epilepsy diagnosis, Artificial Intelligence (AI), seizure detection, deep learning, machine learning, automated EEG analysis, neuroimaging. Original research studies, case reports, and review articles were utilized. The search was limited to publications in Polish and English.
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $100
OpenAlex ID: W7128739994