论文标题
基于脑电图的精神分裂症检测使用经常性自动编码器框架
Schizophrenia detection based on EEG using Recurrent Auto-Encoder framework
论文作者
论文摘要
精神分裂症（SZ）是一种严重的精神障碍，可能会严重影响患者的生活质量。近年来，使用脑电图（EEG）基于深度学习（DL）的SZ检测已受到越来越多的关注。在本文中，我们提出了一个端到端的复发自动编码器（RAE）模型来检测SZ。在RAE模型中，将原始数据输入到一个自动编码器中，然后将重建的数据反复输入到同一块中。由自动编码器块提取的代码同时用作分类器块的输入，以区分SZ患者与健康对照组（HC）。在包含14名SZ患者和14名HC受试者的数据集上进行了评估，而所提出的方法在非依赖性实验方案中达到了平均分类精度为81.81％。这项研究表明，RAE的结构能够捕获SZ患者和HC受试者之间的差异特征。
Schizophrenia (SZ) is a serious mental disorder that could seriously affect the patient's quality of life. In recent years, detection of SZ based on deep learning (DL) using electroencephalogram (EEG) has received increasing attention. In this paper, we proposed an end-to-end recurrent auto-encoder (RAE) model to detect SZ. In the RAE model, the raw data was input into one auto-encoder block, and the reconstructed data were recurrently input into the same block. The extracted code by auto-encoder block was simultaneously served as an input of a classifier block to discriminate SZ patients from healthy controls (HC). Evaluated on the dataset containing 14 SZ patients and 14 HC subjects, and the proposed method achieved an average classification accuracy of 81.81% in subject-independent experiment scenario. This study demonstrated that the structure of RAE is able to capture the differential features between SZ patients and HC subjects.
