Fan Yuying, Chen Duo, Wang Hua, Pan Yijie, Peng Xueping, Liu Xueyan, Liu Yunhui
Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, China.
School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, China.
Front Mol Biosci. 2022 Aug 10;9:931688. doi: 10.3389/fmolb.2022.931688. eCollection 2022.
In recent years, the Burden of Amplitudes and Epileptiform Discharges (BASED) score has been used as a reliable, accurate, and feasible electroencephalogram (EEG) grading scale for infantile spasms. However, manual EEG annotation is, in general, very time-consuming, and BASED scoring is no exception. Convolutional neural networks (CNNs) have proven their great potential in many EEG classification problems. However, very few research studies have focused on the use of CNNs for BASED scoring, a challenging but vital task in the diagnosis and treatment of infantile spasms. This study proposes an automatic BASED scoring framework using EEG and a deep CNN. The feasibility of using CNN for automatic BASED scoring was investigated in 36 patients with infantile spasms by annotating their long-term EEG data with four levels of the BASED score (scores 5, 4, 3, and ≤2). In the validation set, the accuracy was 96.9% by applying a multi-layer CNN to classify the EEG data as a 4-label problem. The extensive experiments have demonstrated that our proposed approach offers high accuracy and, hence, is an important step toward an automatic BASED scoring algorithm. To the best of our knowledge, this is the first attempt to use a CNN to construct a BASED-based scoring model.
近年来，振幅和癫痫样放电负担（BASED）评分已被用作一种可靠、准确且可行的婴儿痉挛脑电图（EEG）分级量表。然而，一般来说，EEG的人工标注非常耗时，基于BASED的评分也不例外。卷积神经网络（CNN）已在许多EEG分类问题中证明了其巨大潜力。然而，很少有研究专注于将CNN用于基于BASED的评分，这在婴儿痉挛的诊断和治疗中是一项具有挑战性但至关重要的任务。本研究提出了一种使用EEG和深度CNN的自动基于BASED的评分框架。通过用四个基于BASED的评分级别（评分5、4、3和≤2）对36例婴儿痉挛患者的长期EEG数据进行标注，研究了使用CNN进行自动基于BASED评分的可行性。在验证集中，通过应用多层CNN将EEG数据分类为一个4标签问题，准确率达到了96.9%。大量实验表明，我们提出的方法具有很高的准确率，因此是朝着自动基于BASED评分算法迈出的重要一步。据我们所知，这是首次尝试使用CNN构建基于BASED的评分模型。
