Automated detection of epileptic seizures using multiscale and refined composite multiscale dispersion entropy

Epilepsy is one of the most common neurological disorders. The electroencephalogram (EEG) is a valuable tool for the detection of epileptic seizures. The diagnosis of epilepsy requires the neurologists to continuously monitor the long-term EEG recordings of patients, which is a time-consuming and error-prone procedure. Therefore, automatic epileptic seizure detection becomes essential. Entropy-based methods are widely used for the automated detection of seizures from EEG signals due to the nonlinear and chaotic nature of these signals. In this work, we propose two recently introduced entropy features, multiscale dispersion entropy (MDE) and refined composite multiscale dispersion entropy (RCMDE) for detection of seizures. We assess the ability of MDE and RCMDE to discriminate the normal EEGs of healthy subjects, interictal (in between seizures), and ictal (during seizures) EEGs of epilepsy patients. We also investigate the two parameters namely, number of classes c and embedding dimension m of MDE and RCMDE that provide the best performance for seizure detection. For this purpose, the MDE and RCMDE values are estimated from normal, interictal, and ictal EEG signals, one-way ANOVA test is employed, and significant features are fed to a support vector machine (SVM) classifier. The experimental results demonstrate that both MDE and RCMDE are promising feature extraction methods that can quantify the complexity of EEG signals successfully and the highest classification accuracies were obtained when c=5 and m=3 for both MDE and RCMDE frameworks. Besides, we have compared the proposed MDE and RCMDE classification results with that of multiscale entropy (MSE) and multiscale permutation entropy (MPE) methods that have been previously applied for the study of seizure detection. It was found that MDE and RCMDE contribute significantly in improving the accuracy of seizure detection.