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07.05.2024 | Review Paper

Research progress of epileptic seizure prediction methods based on EEG

verfasst von: Zhongpeng Wang, Xiaoxin Song, Long Chen, Jinxiang Nan, Yulin Sun, Meijun Pang, Kuo Zhang, Xiuyun Liu, Dong Ming

Erschienen in: Cognitive Neurodynamics

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Abstract

At present, at least 30% of refractory epilepsy patients in the world cannot be effectively controlled and treated. The suddenness and unpredictability of seizures greatly affect the physical and mental health and even the life safety of patients, and the realization of early prediction of seizures and the adoption of interventions are of great significance to the improvement of patients’ quality of life. In this paper, we firstly introduce the design process of EEG-based seizure prediction methods, introduce several databases commonly used in the research, and summarize the commonly used methods in pre-processing, feature extraction, classification and identification, and post-processing. Then, based on scalp EEG and intracranial EEG respectively, we reviewed the current status of epileptic seizure prediction research from five commonly used feature analysis methods, and make a comprehensive evaluation of both. Finally, this paper describes the reasons why the current algorithms cannot be applied to the clinic, summarizes their limitations, and gives corresponding suggestions, aiming to provide improvement directions for subsequent research. In addition, deep learning algorithms have emerged in recent years, and this paper also compares the advantages and disadvantages of deep learning algorithms with traditional machine learning methods, in the hope of providing researchers with new technologies and new ideas and making significant breakthroughs in the field of epileptic seizure prediction.

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Titel: Research progress of epileptic seizure prediction methods based on EEG
verfasst von: Zhongpeng Wang
Xiaoxin Song
Long Chen
Jinxiang Nan
Yulin Sun
Meijun Pang
Kuo Zhang
Xiuyun Liu
Dong Ming
Publikationsdatum: 07.05.2024
Verlag: Springer Netherlands
Erschienen in: Cognitive Neurodynamics
Print ISSN: 1871-4080
Elektronische ISSN: 1871-4099
DOI: https://doi.org/10.1007/s11571-024-10109-w

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