Abstract
In the diagnosis of epileptic seizures, classification is an important step that directly affects the results. Visual inspection of Electroencephalogram (EEG) is a relatively common analytic method of epilepsy, but it is costly, time-consuming and relies on the experiences of the doctor. Therefore, the development of an efficient and accurate epileptic seizure automatic diagnosis system suitable for clinical diagnosis has become an urgent task. In order to better solve the problem of early diagnosis of epileptic and bring timely treatment to patients, the comprehensive representation of k nearest neighbors for multi-distance decision making (CRMKNN) is proposed in this research. In the proposed scheme, Euclidean distance and Hassanat distance are firstly used to select neighbors. Subsequently, the similarity distance is obtained through the linear representation of the nearest neighbors, and calculate the distribution of nearest neighbors in the category to get the discrete distance. Finally, the distance based on the comprehensive representation of the category is used to determine the category of the query EEG signal. In order to verify the method, we used the EEG signals from Bonn university public database and conducted experiments on six kinds of EEG combinations. Experimental results showed that our method could automatically detect seizure in all situations with an accuracy of not less than 99.50%. At the same time, compared with the classification results of existing methods, this method is more effective.
Highlights
Epilepsy is a neurological disease that seriously affects the physical and mental health of patients
This section introduces the experimental results, and analyzes the proposed method through the experimental results and methodology. This method uses a combination of two methods in distance selection, and at the same time introduces the strategies of local mean, linear representation and class dispersion
In the CRMKNN, the significant difference from the traditional k-nearest neighbor (KNN) algorithm is that multiple distances were used to select neighbors
Summary
Epilepsy is a neurological disease that seriously affects the physical and mental health of patients. It can cause convulsions, fainting, loss of consciousness, and behavior changes. Its prominent characteristics are frequent seizures [1]. Epilepsy has become one of the most prevalent brain diseases in the world. About 1% of the global population are suffering from the harm caused by epilepsy [2]. There are phase transitions in epileptic seizures. The main phase are preictal, ictal-the time of seizure occurs and interictal-the time between two consecutive seizures [3]. Correct diagnosis of the different stages of epilepsy is essential to give patients timely and effective treatment
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