Nanyang Technological University, Singapore.
Massachusetts General Hospital, Boston MA 02114, USA.
Int J Neural Syst. 2021 May;31(5):2050074. doi: 10.1142/S0129065720500744. Epub 2021 Jan 12.
The diagnosis of epilepsy often relies on a reading of routine scalp electroencephalograms (EEGs). Since seizures are highly unlikely to be detected in a routine scalp EEG, the primary diagnosis depends heavily on the visual evaluation of Interictal Epileptiform Discharges (IEDs). This process is tedious, expert-centered, and delays the treatment plan. Consequently, the development of an automated, fast, and reliable epileptic EEG diagnostic system is essential. In this study, we propose a system to classify EEG as epileptic or normal based on multiple modalities extracted from the interictal EEG. The ensemble system consists of three components: a Convolutional Neural Network (CNN)-based IED detector, a Template Matching (TM)-based IED detector, and a spectral feature-based classifier. We evaluate the system on datasets from six centers from the USA, Singapore, and India. The system yields a mean Leave-One-Institution-Out (LOIO) cross-validation (CV) area under curve (AUC) of 0.826 (balanced accuracy (BAC) of 76.1%) and Leave-One-Subject-Out (LOSO) CV AUC of 0.812 (BAC of 74.8%). The LOIO results are found to be similar to the interrater agreement (IRA) reported in the literature for epileptic EEG classification. Moreover, as the proposed system can process routine EEGs in a few seconds, it may aid the clinicians in diagnosing epilepsy efficiently.
癫痫的诊断通常依赖于常规头皮脑电图（EEG）的解读。由于在常规头皮 EEG 中极不可能检测到癫痫发作，因此主要诊断主要依赖于对发作间期棘波放电（IEDs）的视觉评估。这个过程繁琐、以专家为中心，并且会延迟治疗计划。因此，开发一种自动化、快速且可靠的癫痫 EEG 诊断系统至关重要。在这项研究中，我们提出了一种基于从发作间期 EEG 中提取的多种模态来对 EEG 进行癫痫或正常分类的系统。该集成系统由三个组件组成：基于卷积神经网络（CNN）的 IED 检测器、基于模板匹配（TM）的 IED 检测器和基于频谱特征的分类器。我们在来自美国、新加坡和印度的六个中心的数据集上评估了该系统。该系统在留一机构外（LOIO）交叉验证（CV）中的平均曲线下面积（AUC）为 0.826（平衡准确率（BAC）为 76.1%），留一受试者外（LOSO）CV AUC 为 0.812（BAC 为 74.8%）。LOIO 结果与文献中报道的癫痫 EEG 分类的组内一致性（IRA）相似。此外，由于所提出的系统可以在几秒钟内处理常规 EEG，因此它可能有助于临床医生有效地诊断癫痫。
