Aung Si Thu, Wongsawat Yodchanan
Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Salaya, Thailand.
Front Physiol. 2020 Jun 25;11:607. doi: 10.3389/fphys.2020.00607. eCollection 2020.
Epilepsy is one of the most common chronic neurological disorders, and therefore, diagnosis and treatment methods are urgently needed for these patients. Many methods and algorithms that can detect seizures in epileptic patients have been proposed. Electroencephalogram (EEG) is one of helpful tools for investigating epilepsy forms in patients, however, an expert in the neurological field must perform a visual inspection to identify a seizure. Such analyses require longer time because of the huge dataset recorded from many electrodes which are put on the human scalp. With the non-stationary nature of EEG, especially during the abnormality periods, entropy measures gain more interest in the field. In this work, by exploring the advantages of both reliable state-of-the-art entropies, fuzzy entropy and distribution entropy, a modified-Distribution entropy (mDistEn) for epilepsy detection is proposed. As the results, the proposed mDistEn method can successfully achieve the same consistency and better accuracy than using the state-of-the-art entropies. The mDistEn corresponds to higher Area Under the Curve (AUC) values compared with the fuzzy entropy and the distribution entropy and yields 92% classification accuracy.
癫痫是最常见的慢性神经系统疾病之一，因此，这些患者迫切需要诊断和治疗方法。已经提出了许多能够检测癫痫患者癫痫发作的方法和算法。脑电图（EEG）是研究患者癫痫形式的有用工具之一，然而，神经领域的专家必须进行目视检查以识别癫痫发作。由于从放置在人头皮上的许多电极记录的数据集庞大，此类分析需要更长时间。鉴于脑电图的非平稳特性，尤其是在异常期间，熵度量在该领域更受关注。在这项工作中，通过探索可靠的最新熵（模糊熵和分布熵）的优势，提出了一种用于癫痫检测的改进分布熵（mDistEn）。结果表明，所提出的mDistEn方法能够成功实现与使用最新熵相同的一致性且具有更高的准确性。与模糊熵和分布熵相比，mDistEn对应的曲线下面积（AUC）值更高，分类准确率达92%。
