Abstract

Epileptic EEG signal recognition is an important method for epilepsy detection. In essence, epileptic EEG signal recognition is a typical imbalanced classification task. However, traditional machine learning methods used for imbalanced epileptic EEG signal recognition face many challenges: (1) traditional machine learning methods often ignore the imbalance of epileptic EEG signals, which leads to misclassification of positive samples and may cause serious consequences and (2) the existing imbalanced classification methods ignore the interrelationship between samples, resulting in poor classification performance. To overcome these challenges, a graph‐based extreme learning machine method (G‐ELM) is proposed for imbalanced epileptic EEG signal recognition. The proposed method uses graph theory to construct a relationship graph of samples according to data distribution. Then, a model combining the relationship graph and ELM is constructed; it inherits the rapid learning and good generalization capabilities of ELM and improves the classification performance. Experiments on a real imbalanced epileptic EEG dataset demonstrated the effectiveness and applicability of the proposed method.

Highlights

  • Epilepsy is a common neurological disease that can cause recurrent seizures

  • In this study, inspired by weighted extreme learning machine (WELM), we propose a novel graph-based extreme learning machine (ELM) (G-ELM) for imbalanced epileptic EEG signal recognition

  • It includes the epileptic EEG dataset, the feature extraction methods, and the classical ELM, which are used for epileptic EEG signal detection

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Summary

Introduction

Epilepsy is a common neurological disease that can cause recurrent seizures. During seizures, injury or life-threatening events may occur owing to the distraction or involuntary spasms of the patient [1, 2]. In the clinical diagnosis of various seizures, electroencephalogram (EEG) signal detection plays a crucial role [3]. This is because the epileptic brain releases characteristic waves during seizures. An increasing number of machine learning-based methods have been applied for epileptic EEG signal recognition [4,5,6,7,8]. We briefly describe the background related to the proposed epileptic EEG signal recognition method. Groups C–E are segments acquired from volunteers with epilepsy.

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