Abstract
Epilepsy is one of the most common neurological disorders typically characterized by recurrent and uncontrollable seizures, which seriously affects the quality of life of epilepsy patients. The effective tool utilized in the clinical diagnosis of epilepsy is the Electroencephalogram (EEG). The emergence of machine learning promotes the development of automated epilepsy detection techniques. New algorithms are continuously introduced to shorten the detection time and improve classification accuracy. This minireview summarized the latest research of epilepsy detection techniques that focused on acquiring, preprocessing, feature extraction, and classification of epileptic EEG signals. The application of seizure prediction and localization based on EEG signals in the diagnosis of epilepsy was also introduced. And then, the future development trend of epilepsy detection technology has prospected at the end of the article.
Highlights
Epilepsy is a neurological disorder caused by the sudden abnormal discharge of brain neurons
Seizure detection has promoted the development of seizure prediction and location. This minireview introduced the decisive steps of epileptic seizure detection shown in Figure 1, including the acquisition, preprocessing, feature extraction, and classification of epilepsy EEG signals, besides the application of seizure prediction and localization in the diagnosis of epilepsy and trend of future seizure detection techniques was given here
Since the beginning of the twenty-first century, the rapid development of artificial intelligence and machine learning, epilepsy detection techniques based on EEG signals has attracted more and more attention from researchers
Summary
Epilepsy is a neurological disorder caused by the sudden abnormal discharge of brain neurons. For patients with refractory epilepsy, Vagus nerve stimulation (VNS) has a significant therapeutic effect (Shimogawa et al, 2021) Under such circumstances, using machine learning algorithms in EEG signals to realize epileptic detection, realizing the treatment effect evaluation, will help clinicians treat epileptic (Assi et al, 2017; Al-Hadeethi et al, 2020). Seizure detection has promoted the development of seizure prediction and location This minireview introduced the decisive steps of epileptic seizure detection shown, including the acquisition, preprocessing, feature extraction, and classification of epilepsy EEG signals, besides the application of seizure prediction and localization in the diagnosis of epilepsy and trend of future seizure detection techniques was given here This minireview introduced the decisive steps of epileptic seizure detection shown in Figure 1, including the acquisition, preprocessing, feature extraction, and classification of epilepsy EEG signals, besides the application of seizure prediction and localization in the diagnosis of epilepsy and trend of future seizure detection techniques was given here
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