Robust Epileptic Seizure Detection Using Multiscale Distribution Entropy Analysis for Short EEG Recordings

In the world, epilepsy is a common neurological disorder, and around 50 million people have epilepsy. The risk of premature death in epileptic patients is up to 3 times higher than the general population. To improve epilepsy patients’ quality of life, the use of non-invasive brain rhythm, i.e., electroencephalogram (EEG) has an important role in detecting an epileptic seizure that is the hallmark of epilepsy. By measuring the complexity of the EEG signals from patients, various entropy methods are used for detecting a variety of types of epileptic seizures. Conventional entropy methods such as the Approximate Entropy (ApEn) and Sample Entropy (SampEn) are dependent on data length and predetermined parameters. Here, we present a multiscale extension of Distribution Entropy (DistEn) that addresses the disadvantages of conventional entropy measures, which is referred to as multiscale DistEn (MDE). The proposed MDE is composed of a moving averaging procedure and DistEn estimation to reflect the reliable complexities over multiple temporal scales for short length EEG signals. The validation of the performance of MDE using actual normal and epileptic EEG signals is carried out. The experimental results show that MDE yields an outstanding performance in distinguishing the ictal EEG recordings compared to other entropy measures for short EEG recordings.