Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine

► We propose a novel optimized sample entropy algorithm for epileptic seizure detection. ► Sample entropy with high statistical significance represents the characteristics of epileptic EEGs. ► Optimal parameters of sample entropy are found for epileptic EEG signal analysis. ► A sample entropy-extreme learning machine framework is proposed for detecting epileptic seizures in EEGs. ► The proposed method achieves not only a high detection accuracy but also a very fast computation speed. Epilepsy is one of the most common neurological disorders – approximately one in every 100 people worldwide are suffering from it. The electroencephalogram (EEG) is the most common source of information used to monitor, diagnose and manage neurological disorders related to epilepsy. Large amounts of data are produced by EEG monitoring devices, and analysis by visual inspection of long recordings of EEG in order to find traces of epilepsy is not routinely possible. Therefore, automated detection of epilepsy has been a goal of many researchers for a long time. This paper presents a novel method for automatic epileptic seizure detection. An optimized sample entropy (O-SampEn) algorithm is proposed and combined with extreme learning machine (ELM) to identify the EEG signals regarding the existence of seizure or not. To the knowledge of the authors, there exists no similar work in the literature. A public dataset was utilized for evaluating the proposed method. Results show that the proposed epilepsy detection approach achieves not only high detection accuracy but also a very fast computation speed, which demonstrates its huge potential for the real-time detection of epileptic seizures.