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%.
Highlights
Biological signals are the complex signals resulting from some complex physiological processes in the body [1]
Multidistance Signal Level Difference (MSLD) calculated the absolute value of the difference of 2 data samples at distance d so that the resulting signal was always in the form of a positive value
New signals generated by MSLD would have a number of properties slightly different from the original signal, and these features would be quantized using sample entropy
Summary
Biological signals are the complex signals resulting from some complex physiological processes in the body [1]. Complex signals are signals that have some properties between periodic signals and random signals. These signals are analyzed using several points of view, such as fractal, entropy, or chaotic approaches. One commonly used method for complex signal analysis is multiscale entropy (MSE). Costa et al proposed MSE method for a biological signal analysis [2]. As the biological signals are considered to have a number of multiscale properties, an analysis on multiple scales will provide the complete signal characteristic information
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