Li Qiong, Gao Jianbo, Huang Qi, Wu Yuan, Xu Bo
School of Computer, Electronics and Information, Guangxi University, Nanning, China.
Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University, Beijing, China.
Front Bioeng Biotechnol. 2020 Sep 8;8:1006. doi: 10.3389/fbioe.2020.01006. eCollection 2020.
Epileptiform discharges are of fundamental importance in understanding the physiology of epilepsy. To aid in the clinical diagnosis, classification, prognosis, and treatment of epilepsy, it is important to develop automated computer programs to distinguish epileptiform discharges from normal electroencephalogram (EEG). This is a challenging task as clinically used scalp EEG often contains a lot of noise and motion artifacts. The challenge is even greater if one wishes to develop explainable rather than black-box based approaches. To take on this challenge, we propose to use a multiscale complexity measure, the scale-dependent Lyapunov exponent (SDLE). We analyzed 640 multi-channel EEG segments, each 4 long. Among these segments, 540 are short epileptiform discharges, and 100 are from healthy controls. We found that features from SDLE were very effective in distinguishing epileptiform discharges from normal EEG. Using Random Forest Classifier (RF) and Support Vector Machines (SVM), the proposed approach with different features from SDLE robustly achieves an accuracy exceeding 99% in distinguishing epileptiform discharges from normal control ones. A single parameter, which is the ratio of the spectral energy of EEG signals and the SDLE and quantifies the regularity or predictability of the EEG signals, is introduced to better understand the high accuracy in the classification. It is found that this regularity is considerably greater for epileptiform discharges than for normal controls. Robustly having high accuracy in distinguishing epileptiform discharges from normal controls irrespective of which classification scheme being used, the proposed approach has the potential to be used widely in a clinical setting.
癫痫样放电对于理解癫痫的生理学至关重要。为了辅助癫痫的临床诊断、分类、预后和治疗，开发自动化计算机程序以区分癫痫样放电与正常脑电图（EEG）非常重要。这是一项具有挑战性的任务，因为临床使用的头皮脑电图通常包含大量噪声和运动伪影。如果希望开发可解释而非基于黑箱的方法，挑战会更大。为了应对这一挑战，我们建议使用一种多尺度复杂性度量，即尺度相关李雅普诺夫指数（SDLE）。我们分析了640个多通道脑电图片段，每个片段时长4秒。在这些片段中，540个是短暂癫痫样放电，100个来自健康对照。我们发现，SDLE的特征在区分癫痫样放电与正常脑电图方面非常有效。使用随机森林分类器（RF）和支持向量机（SVM），所提出的方法结合SDLE的不同特征，在区分癫痫样放电与正常对照时稳健地实现了超过99%的准确率。引入一个单一参数，即脑电图信号的频谱能量与SDLE的比值，以量化脑电图信号的规律性或可预测性，从而更好地理解分类中的高精度。结果发现，癫痫样放电的这种规律性明显高于正常对照。无论使用哪种分类方案，所提出的方法在区分癫痫样放电与正常对照方面都具有稳健的高精度，具有在临床环境中广泛应用的潜力。
