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.

中文翻译：

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基于发作间期头皮脑电图特征的成人癫痫自动化诊断工具：一项六中心研究

癫痫的诊断通常依赖于常规头皮脑电图 (EEG) 的读数。由于在常规头皮脑电图中极不可能检测到癫痫发作，因此初步诊断在很大程度上取决于对发作间期癫痫样放电 (IED) 的视觉评估。这个过程繁琐，以专家为中心，并且延误了治疗计划。因此，开发自动化、快速、可靠的癫痫脑电图诊断系统至关重要。在这项研究中，我们提出了一个系统，基于从发作间期 EEG 中提取的多种模式将 EEG 分类为癫痫或正常。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 检测器和基于光谱特征的分类器。我们根据来自美国、新加坡和印度的六个中心的数据集评估该系统。该系统产生的平均留一制度留出 (LOIO) 交叉验证 (CV) 曲线下面积 (AUC) 为 0.826（平衡准确度 (BAC) 为 76.1%）和留一主题留出 (LOSO) CV AUC 为 0.812（BAC 为 74.8%）。发现 LOIO 结果与文献中报道的癫痫脑电图分类的评估者协议 (IRA) 相似。此外，由于所提出的系统可以在几秒钟内处理常规脑电图，它可以帮助临床医生有效地诊断癫痫。发现 LOIO 结果与文献中报道的癫痫脑电图分类的评估者协议 (IRA) 相似。此外，由于所提出的系统可以在几秒钟内处理常规脑电图，它可以帮助临床医生有效地诊断癫痫。发现 LOIO 结果与文献中报道的癫痫脑电图分类的评估者协议 (IRA) 相似。此外，由于所提出的系统可以在几秒钟内处理常规脑电图，它可以帮助临床医生有效地诊断癫痫。