Sample Entropy on Multidistance Signal Level Difference for Epileptic EEG Classification

Abstract

Epilepsy is a disorder of the brain's nerves as a result of excessive brain cell activity. It is generally characterized by the recurrent unprovoked seizures. This neurological abnormality can be detected and evaluated using Electroencephalogram (EEG) signal. Many algorithms have been applied to achieve high performance for the EEG classification of epileptic. However, the complexity and randomness of EEG signals become a challenge to researchers in applying the appropriate algorithms. In this research, sample entropy on Multidistance Signal Level Difference (MSLD) was applied to obtain the characteristic of EEG signals, especially towards the epilepsy patients. The test was performed on three classes of EEG data: EEG signals of epilepsy patient in ictal (seizure), interictal conditions (occurring between seizures) and normal EEG signals from healthy subjects with a closed eye condition. In this study, classification and verification were done using the Support Vector Machine (SVM) method. Through the 5-fold cross-validation, experimental results showed the highest accuracy of 97.7%.

One-Lead Electrocardiogram for Biometric Authentication using Time Series Analysis and Support Vector Machine

“…SampEn is one method that is widely used to measure signal complexity. In a research conducted by Rizal [17], it was proven that SampEn can provide high accuracy in the case of epileptic EEG classifications.…”

“…This proposed study focuses on time series analysis methods using Hjorth Descriptor and Sample Entropy for ECG biometrics. These methods have been selected for having good performance based upon some previous research to classify ECG and Epileptic EEG signals [15]- [17]. Both of these methods are basically used for analysis of signal complexity.…”

Hybrid Feature Extraction Technique for Multi-Classification of Ictal and Non-Ictal EEG Epilepsy Signals

“…Recently, some researches focus on nonlinear techniques to capture the nonlinearity nature of the EEG epilepsy signal. Different types of entropy have been investigated such as approximate entropy [38][39][40], sample entropy [41]. Empirical mode decomposition is used to extract statistical features [42].…”

DWT-Net: Seizure Detection System with Structured EEG Montage and Multiple Feature Extractor in Convolution Neural Network

“…The problem of automatic seizure detection can be divided into two main steps, feature extraction and classifier training. Considerable amounts of works have been done with this two-step procedure for better detection accuracy, including time-frequency feature map with a support vector machine (SVM) [3,7], nonlinear features with different types of classifiers [5,8,9], and features based on time-frequency image with image recognition methods [10,11]. These researches have provided different methodologies for seizure detection.…”