Interictal epileptiform discharges (IEDs) are elicited from an epileptic brain, whereas they can also be due to other neurological abnormalities. The diversity in their morphologies, their strengths, and their sources within the brain cause a great deal of uncertainty in their labeling by clinicians. The aim of this study is therefore to exploit and incorporate this uncertainty (the probability of the waveform being an IED) in the IED detection system which combines spatial component analysis (SCA) with the IED probabilities referred to as SCA-IEDP-based method. For comparison, we also propose and study SCA-based method in which probability of the waveform being an IED is ignored. The proposed models are employed to detect IEDs in two different classification approaches: (1) subject-dependent and (2) subject-independent classification approaches. The proposed methods are compared with two other state-of-the-art methods namely, time–frequency features and tensor factorization methods. The proposed SCA-IEDP model has achieved superior performance in comparison with the traditional SCA and other competing methods. It achieved 79.9% and 63.4% accuracy values in subject-dependent and subject-independent classification approaches, respectively. This shows that considering the IED probabilities in designing an IED detection system can boost its performance.

中文翻译：

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通过多向分析将数据标记中的不确定性纳入并发头皮脑电图发作间期癫痫样放电的自动检测中

发作间期癫痫样放电 (IED) 是由癫痫大脑诱发的，而它们也可能是由于其他神经系统异常所致。它们的形态、优势和脑内来源的多样性导致临床医生对其标签的不确定性很大。因此，本研究的目的是在 IED 检测系统中利用并结合这种不确定性（波形是 IED 的概率），该系统结合了空间分量分析 (SCA) 和 IED 概率，称为基于 SCA-IEDP 的方法。为了比较，我们还提出并研究了忽略波形为 IED 的概率的基于 SCA 的方法。所提出的模型用于以两种不同的分类方法检测 IED：（1）依赖于主题的分类方法和（2）独立于主题的分类方法。将所提出的方法与其他两种最先进的方法进行比较，即时频特征和张量分解方法。与传统的 SCA 和其他竞争方法相比，所提出的 SCA-IEDP 模型取得了优越的性能。它在依赖于主题和独立于主题的分类方法中分别实现了 79.9% 和 63.4% 的准确度值。这表明在设计 IED 检测系统时考虑 IED 概率可以提高其性能。在依赖于主题和独立于主题的分类方法中，准确度值分别为 4%。这表明在设计 IED 检测系统时考虑 IED 概率可以提高其性能。在依赖于主题和独立于主题的分类方法中，准确度值分别为 4%。这表明在设计 IED 检测系统时考虑 IED 概率可以提高其性能。