Presurgical investigations for categorizing focal patterns are crucial, leading to localization and surgical removal of the epileptic focus. This paper presents a machine learning approach using information theoretic features extracted from high-frequency subbands to detect the epileptic focus from interictal intracranial electroencephalogram (iEEG). It is known that high-frequency subbands (>80 Hz) include important biomarkers such as high-frequency oscillations (HFOs) for identifying epileptic focus commonly referred to as the seizure onset zone (SOZ). In this analysis, the multi-channel interictal iEEG signals were splitted into segments and each segment was decomposed into multiple high-frequency subbands. The different types of entropy were calculated for each of the subbands and the sparse linear discriminant analysis (sLDA) was applied to select the prominent entropy features. Due to the imbalance of SOZ and non-SOZ channels in iEEG data, the use of machine learning techniques is always tricky. To deal with the imbalanced learning problem, an adaptive synthetic oversampling approach (ADASYN) with radial basis function kernel-based SVM was used to detect the focal segments. Finally, the epileptic focus was identified based on detection of focal segments on SOZ and non-SOZ channels. Eight patients were examined to observe the efficiency of the automatic detector. The experimental results and statistical tests indicate that the proposed automatic detector can identify the epileptic focus accurately and efficiently.

译文

对局灶性模式进行分类的术前研究至关重要,从而导致癫痫灶的定位和手术切除。本文提出了一种机器学习方法,该方法使用从高频子带中提取的信息理论特征来检测发作期颅内脑电图 (iEEG) 中的癫痫灶。众所周知,高频子带 (>80 hz) 包括重要的生物标志物,例如用于识别癫痫灶的高频振荡 (HFOs),通常称为癫痫发作区 (SOZ)。在此分析中,将多通道interictal iEEG信号分成多个段,并将每个段分解为多个高频子带。为每个子带计算不同类型的熵,并应用稀疏线性判别分析 (sLDA) 来选择突出的熵特征。由于iEEG数据中SOZ和非SOZ通道的不平衡,机器学习技术的使用总是很棘手。为了解决不平衡学习问题,使用了具有基于径向基函数核的SVM的自适应综合过采样方法 (ADASYN) 来检测焦点片段。最后,根据对SOZ和非SOZ通道上的灶段的检测,确定了癫痫灶。对八名患者进行了检查,以观察自动检测器的效率。实验结果和统计测试表明,所提出的自动检测器可以准确有效地识别癫痫灶。

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