基于深度学习的癫痫脑电不平衡分类方法

癫痫发作自动检测技术对癫痫患者的诊断和治疗具有重要意义。由于癫痫发作期持续时间较短,发作期与非发作期的脑电数据分布是不平衡的。针对该问题,本文提出了一种不平衡分类与深度学习相结合的癫痫发作自动检测方法。首先,为防止不同类别数据之间界限模糊,使用Borderline-SMOTE算法对1/3训练集做平衡处理;之后,设计了金字塔型的一维深度卷积神经网络,并利用平衡处理的训练集进行训练。与常见的二维卷积神经网络不同,本文构造的一维卷积神经网络减少了训练参数,提高了训练速率,能够有效地避免由于训练样本较少而造成的过拟合。在长达991小时的长程头皮脑电数据集上的实验表明,经过平衡处理后的检测效果得到明显改善,最佳敏感度达到92.35%,特异性达到99.88%,阳性预测率达到90.68%,阴性预测率达到99.91%。同时,与其他癫痫检测方法的比较表明,本文方法具有更好的检测结果,更加符合临床应用的要求。

Imbalanced classification for epileptic EEG signals based on deep learning

Automatic seizure detection is of great significance to the diagnosis and treatment of patients with epilepsy. Due to the short duration of epileptic seizure period, the EEG signal distribution between the seizure period and the non-seizure period is imbalanced. To solve this problem, an automatic detection method of epilepsy based on the fusion of imbalanced classification and deep learning is proposed. Firstly, the Borderline-SMOTE algorithm is applied to one-third training set to prevent the boundaries between different classes from blurring. Then, a pyramidal one-dimensional convolutional neural network is designed, which is trained with the balanced processing data. Different from the common 2D convolutional neural network, the 1D convolutional neural network reduces the number of training parameters. The training rate is improved, and the overfitting is avoided effectively which is caused by the small number of training samples. By utilizing the 991 hours long scalp EEG database, the effectiveness of the seizure detection after balanced treatment is significantly improved. The sensitivity, specificity, positive predictive value, and negative predictive value reach 92. 35% , 99. 88% , 90. 68% , and 99. 91% , respectively. Meanwhile, the comparison with other seizure detection methods shows that the proposed method has better performance. It is suitable for satisfying requirements of clinical application.