College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
College of Software, Taiyuan University of Technology, Taiyuan, China.
Sci Rep. 2021 Feb 25;11(1):4706. doi: 10.1038/s41598-021-83350-6.
Schizophrenia is a serious mental illness that causes great harm to patients, so timely and accurate detection is essential. This study aimed to identify a better feature to represent electroencephalography (EEG) signals and improve the classification accuracy of patients with schizophrenia and healthy controls by using EEG signals. Our research method involves two steps. First, the EEG time series is preprocessed, and the extracted time-domain and frequency-domain features are transformed into a sequence of red-green-blue (RGB) images that carry spatial information. Second, we construct hybrid deep neural networks (DNNs) that combine convolution neural networks and long short-term memory to address RGB images to classify schizophrenic patients and healthy controls. The results show that the fuzzy entropy (FuzzyEn) feature is more significant than the fast Fourier transform (FFT) feature in brain topography. The deep learning (DL) method that we propose achieves an average accuracy of 99.22% with FuzzyEn and an average accuracy of 96.34% with FFT. These results show that the best effect is to extract fuzzy features as input features from EEG time series and then use a hybrid DNN for classification. Compared with the most advanced methods in this field, significant improvements have been achieved.
精神分裂症是一种严重的精神疾病，会对患者造成极大的伤害，因此及时、准确的检测至关重要。本研究旨在通过使用脑电图（EEG）信号，找到更好的特征来表示脑电图信号，从而提高精神分裂症患者和健康对照组的分类准确性。我们的研究方法包括两个步骤。首先，对 EEG 时间序列进行预处理，并提取时域和频域特征，将其转换为携带空间信息的红绿蓝（RGB）图像序列。其次，我们构建了结合卷积神经网络和长短时记忆的混合深度神经网络（DNN），以解决 RGB 图像分类精神分裂症患者和健康对照组的问题。结果表明，在脑地形图中，模糊熵（FuzzyEn）特征比快速傅里叶变换（FFT）特征更为显著。我们提出的深度学习（DL）方法使用 FuzzyEn 的平均准确率为 99.22%，使用 FFT 的平均准确率为 96.34%。这些结果表明，从 EEG 时间序列中提取模糊特征作为输入特征，然后使用混合 DNN 进行分类，效果最佳。与该领域最先进的方法相比，我们取得了显著的改进。
