Artificial Intelligence-Enabled EEG Signal Processing-Based Detection of Epileptic Seizures
Muhammed Enes Subasi Emrah Hancer
Muhammed Enes Subasi
Emrah Hancer
Citations
Altmetric:
Type
Supervisor
Subject
Date
2024-04
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Epilepsy affects numerous people worldwide. Electroencephalography (EEG) is an important tool in the diagnosis of epilepsy. Real-time seizure onset detection is critical for accurate evaluation, presurgical assessment, seizure prevention, and emergency warnings and overall improving patients’ quality of life, but manually examining EEG signals is tedious and time-consuming. To assist neurologists, many automatic systems have been proposed to support neurologists utilizing conventional techniques, and these have performed well in detecting epilepsy. Big data applications, particularly biomedical signals, are becoming more appealing in this era as data collection and storage have expanded in recent years. Because data mining approaches are not adaptable to the new needs, big data processing to extract knowledge is difficult. In this chapter, we review AI-enabled signal processing-based approaches for detecting epileptic seizures using EEG signals including with examples.
Department
Publisher
Sponsor
Copyright
Book title
Artificial Intelligence Enabled Signal Processing based Models for Neural Information Processing