Epilepsy detection and analysis method for specific patient based on data augmentation and deep learning

近年来，采用脑电波进行癫痫发作检测得到了学术界的广泛关注，而用于癫痫发作检测的脑电波数据存在数据采集困难、发作样本少等问题，在训练样本量不足的情况下采用深度学习容易产生过拟合现象。为了解决此问题，本文以美国波士顿儿童医院的癫痫脑电数据集为研究对象，将小波变换用于数据增强，通过设置不同的小波变换尺度因子来生成相应的数据，达到成倍增加训练样本的目的；另外，在模型设计方面，本文结合深度学习、集成学习和迁移学习等方法，提出在训练样本量不足的情况下针对特定癫痫患者的具有较高检测准确率的癫痫检测方法。在测试中，本文分析了以小波变换尺度因子为2、4、8时的癫痫发作检测实验结果，在小波尺度因子为8时，平均准确率、平均敏感度、平均特异性分别为95.47%、93.89%和96.48%；另外，通过与近期相关文献进行对比，验证了本文方法具有一定的优越性。本研究结果可为癫痫检测的临床应用提供借鉴。.; In recent years, epileptic seizure detection based on electroencephalogram (EEG) has attracted the widespread attention of the academic. However, it is difficult to collect data from epileptic seizure, and it is easy to cause over fitting phenomenon under the condition of few training data. In order to solve this problem, this paper took the CHB-MIT epilepsy EEG dataset from Boston Children's Hospital as the research object, and applied wavelet transform for data augmentation by setting different wavelet transform scale factors. In addition, by combining deep learning, ensemble learning, transfer learning and other methods, an epilepsy detection method with high accuracy for specific epilepsy patients was proposed under the condition of insufficient learning samples. In test, the wavelet transform scale factors 2, 4 and 8 were set for experimental comparison and verification. When the wavelet scale factor was 8, the average accuracy, average sensitivity and average specificity was 95.47%, 93.89% and 96.48%, respectively. Through comparative experiments with recent relevant literatures, the advantages of the proposed method were verified. Our results might provide reference for the clinical application of epilepsy detection.