基于同步压缩和DCGAN的癫痫脑电信号检测方法

脑电信号智能识别是癫痫病检测的重要手段，为更加准确地预测癫痫发作，针对目前的深度学习方法特别是卷积神经网络在脑电信号分类方面存在的一些问题，如算法复杂度过高、样本量太少导致分类效果差等，提出基于傅里叶同步压缩变换和深度卷积生成对抗网络的癫痫脑电信号检测方法。首先同步压缩方法将短时傅里叶变换处理后的信号时频能量进行压缩，使得频谱图像精度更高；其次构建深度卷积生成对抗网络来提取特征;最后实现癫痫发作预测。实验在CHB-MIT脑电数据集上进行，结果表明该方法具有97.9%的检测准确率。使用生成对抗网络有效解决了样本量不足的问题，结合同步压缩处理方法后，具有良好的识别准确性。

Epileptic EEG detection based onsynchrosqueezing transform and DCGAN

Intelligent recognition of electroencephalogram (EEG) signals is an important means of epilepsy detection. Current deep learning methods, especially convolutional neural networks, have some problems in the classification of EEG signals. For example, the algorithm complexity is too high, the sample size is too small, and the classification effect is poor. In order to predict seizures more accurately, an epileptic EEG detection method based on Fourier-based synchro squeezing transform and deep convolution generative adversarial network is proposed. Firstly, the synchrosqueezing transform compresses the time-frequency energy processed by the short-time Fourier transform to the real instantaneous position, so that the frequency curve is more concentrated. Secondly, a deep convolution generation confrontation network is constructed as a feature extractor. Finally, epileptic seizure prediction is achieved. The experiment was conducted on the CHB-MIT EEG data set. Experimental results show that this method has a high classification accuracy rate of up to 97.9%. The use of generative adversarial networks effectively solves the problem of insufficient sample size. Combined with the synchrosqueezing transform preprocessing method, it has high recognition accuracy.