Incorporating Uncertainty in Data Labeling into Automatic Detection of Interictal Epileptiform Discharges from Concurrent Scalp-EEG via Multi-way Analysis.
Incorporating Uncertainty in Data Labeling into Automatic Detection of Interictal Epileptiform Discharges from Concurrent Scalp-EEG via Multi-way Analysis.
复制标题
通过多路分析将数据标记中的不确定性纳入同步头皮脑电图发作间期癫痫样放电的自动检测中。
DOI：
10.1142/s0129065721500192
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发表时间：
2021
影响因子：
8
通讯作者：
Abdi-Sargezeh B
中科院分区：
文献类型：
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作者：
Abdi-Sargezeh B
发作间期癫痫样放电（IED）是由癫痫大脑引起的，然而它们也可能是由于其他神经异常引起的。它们在大脑中的形态、强度和来源的多样性导致临床医生在标记它们时存在很大的不确定性。因此，本研究的目的是利用和纳入这种不确定性（波形的概率是一个IED）的IED检测系统，结合空间分量分析（SCA）与IED概率称为SCA-IEDP为基础的方法。为了比较，我们还提出并研究了基于SCA的方法，其中波形是IED的概率被忽略。所提出的模型被用来检测IED在两种不同的分类方法：（1）主题相关和（2）主题无关的分类方法。所提出的方法进行了比较与其他两个国家的最先进的方法，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，SCA-IEDP模型具有上级性能。它实现了79.9%和63.4%的准确度值，分别在受试者相关和受试者无关的分类方法。这表明，在设计IED检测系统时考虑IED概率可以提高其性能。
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.