Akter Most Sheuli, Islam Md Rabiul, Tanaka Toshihisa, Iimura Yasushi, Mitsuhashi Takumi, Sugano Hidenori, Wang Duo, Molla Md Khademul Islam
Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan.
Institute of Global Innovation Research, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan.
Entropy (Basel). 2020 Dec 15;22(12):1415. doi: 10.3390/e22121415.
The design of a computer-aided system for identifying the seizure onset zone (SOZ) from interictal and ictal electroencephalograms (EEGs) is desired by epileptologists. This study aims to introduce the statistical features of high-frequency components (HFCs) in interictal intracranial electroencephalograms (iEEGs) to identify the possible seizure onset zone (SOZ) channels. It is known that the activity of HFCs in interictal iEEGs, including ripple and fast ripple bands, is associated with epileptic seizures. This paper proposes to decompose multi-channel interictal iEEG signals into a number of subbands. For every 20 s segment, twelve features are computed from each subband. A mutual information (MI)-based method with grid search was applied to select the most prominent bands and features. A gradient-boosting decision tree-based algorithm called LightGBM was used to score each segment of the channels and these were averaged together to achieve a final score for each channel. The possible SOZ channels were localized based on the higher value channels. The experimental results with eleven epilepsy patients were tested to observe the efficiency of the proposed design compared to the state-of-the-art methods.
癫痫学家期望设计一种计算机辅助系统，用于从发作间期和发作期脑电图（EEG）中识别癫痫发作起始区（SOZ）。本研究旨在介绍发作间期颅内脑电图（iEEG）中高频成分（HFC）的统计特征，以识别可能的癫痫发作起始区（SOZ）通道。已知发作间期iEEG中HFC的活动，包括涟漪波和快涟漪波段，与癫痫发作有关。本文提出将多通道发作间期iEEG信号分解为多个子带。对于每20秒的片段，从每个子带计算十二个特征。应用基于互信息（MI）的网格搜索方法来选择最突出的频段和特征。使用一种名为LightGBM的基于梯度提升决策树的算法对通道的每个片段进行评分，并将这些评分平均起来以获得每个通道的最终分数。基于较高分数的通道来定位可能的SOZ通道。对11名癫痫患者的实验结果进行了测试，以观察与现有最先进方法相比，所提出设计的效率。
