Department of Biomedical Engineering, Science and Research Branch Islamic Azad University, Tehran, Iran.
Department of Clinical Neurological Sciences, The University of Western Ontario, Canada.
Epilepsy Res. 2020 Nov;167:106483. doi: 10.1016/j.eplepsyres.2020.106483. Epub 2020 Oct 6.
Automatic detection of epileptic seizures can serve as a valuable clinical tool which involves a more objective and computationally efficient method for the analysis of EEG data in order to generate increasingly accurate and reliable results. Automatic seizure detection is also an important component of closed-loop responsive cortical stimulation systems. The goal of this study is to evaluate EEG-based features recently proposed for seizure detection to discover the optimum ones for a reliable seizure detection system. We extracted seizure detection features from intracranial EEG signals that were recorded during invasive pre-surgical epilepsy monitoring of people with drug resistant focal epilepsy at the Epilepsy Center of the University Hospital of Freiburg. Features from time, frequency and phase space domains as well as similarity/dissimilarity features were considered. The performance of each feature was investigated using the statistical test ANOVA. Performance analysis was conducted separately on the recordings from the channels within the seizure-onset zone (SOZ-in) and the recordings from the channels outside the seizure-onset zone (SOZ-out). Similarity/dissimilarity features that measure dynamic properties of the EEG signal and the evolving phenomena of the seizures could significantly separate ictal (during seizure) states from pre-ictal (before seizure) states (p < 0.01). Among them, our proposed feature, Bhattacharyya-based dissimilarity index (BBDI), successfully passed Tukey's post-hoc test as well suggesting that it can distinguish both pre-ictal and post-ictal (after seizure) periods from ictal period. BBDI was further applied to detect epileptic seizures and achieved area under the curve of the receiver-operator characteristic (ROC) equal to 0.96 and 0.94 for SOZ-in and SOZ-out channels, respectively. No significant difference (p = 0.59) was observed in the performance of features between SOZ-in recordings and SOZ-out recordings. The discriminative value of EEG seizure detection features was determined by statistical tests. As a result, the best features to be selected for a reliable seizure detection system designed for people with drug-resistant focal epilepsy were suggested, which include similarity/dissimilarity indices.
癫痫发作的自动检测可以作为一种有价值的临床工具，它涉及到一种更客观和计算效率更高的方法来分析脑电图数据，以产生越来越准确和可靠的结果。自动癫痫发作检测也是闭环反应性皮质刺激系统的重要组成部分。本研究的目的是评估最近提出的基于脑电图的癫痫发作检测特征，以发现用于可靠癫痫发作检测系统的最佳特征。我们从在弗赖堡大学医院的癫痫中心进行的抗药性局灶性癫痫患者的侵入性术前癫痫监测中记录的颅内脑电图信号中提取了癫痫发作检测特征。考虑了时间、频率和相空间域的特征以及相似性/不相似性特征。使用统计检验 ANOVA 研究了每个特征的性能。分别对发作起始区（SOZ-in）内通道的记录和发作起始区外（SOZ-out）通道的记录进行了性能分析。用于测量脑电图信号动态特性和癫痫发作演变现象的相似性/不相似性特征能够显著区分发作期（发作期间）和发作前期（发作前）状态（p < 0.01）。其中，我们提出的基于 Bhattacharyya 的不相似性指数（BBDI）特征成功通过了 Tukey 事后检验，表明它可以区分发作期和发作后期（发作后）。BBDI 进一步用于检测癫痫发作，在 SOZ-in 和 SOZ-out 通道上获得了接收器操作特征（ROC）曲线下面积分别为 0.96 和 0.94。在 SOZ-in 记录和 SOZ-out 记录之间，特征的性能没有显著差异（p = 0.59）。通过统计检验确定了脑电图癫痫发作检测特征的判别值。因此，建议选择用于抗药性局灶性癫痫患者的可靠癫痫发作检测系统的最佳特征，包括相似性/不相似性指数。
