基于深度神经网络的个性化睡眠癫痫发作预测

现有癫痫发作预测方法存在精度较低、错误报警率较高、癫痫患者睡眠脑电特异性、致痫灶位置和类型不同导致脑电信号存在差异的问题.文中提出基于深度神经网络的个性化睡眠癫痫发作预测方法,帮助医生和患者采取及时有效的治疗措施,降低患者患并发症和猝死的概率.对原始脑电信号滤波和分段以去除噪声,保证短时间内触发警报,利用离散小波变换分解信号并提取统计特征表征脑电信号时频特征.再应用双向长短期记忆网络挖掘最具鉴别能力的特征并结合留一法分类,经过决策过程优化得到预测结果.在不同频带限制条件下的实验表明,与睡眠癫痫相关的δ频带信号是影响发作预测性能的重要因素.相比现有睡眠癫痫预测方法,文中方法性能较优.

A Patient-Specific Method for Epileptic Seizure Prediction During Sleep Based on Deep Neural Network

The existing epileptic seizure prediction methods present the problems of low accuracy, high false alarm rate, sleep electroencephalogram(EEG) specificity of epileptic patients and differences in EEG signals caused by differences in the location and type of epileptic foci . In this paper, a patient-specific method for epileptic seizure prediction during sleep based on deep neural network is proposed to help doctors and patients to take timely and effective treatment measures. Consequently, the probability of patients suffering from complications and sudden death is reduced. The original EEG signals are filtered and segmented to remove noise and trigger the alarm in a short time. Discrete wavelet transform is utilized to decompose the EEG, and statistical features are extracted to reveal the time-frequency characteristics of EEG signals. Then, the bi-direction long-short term memory(Bi-LSTM) is employed to mine the most discriminative features combined with the leave-one-out method for classification. The prediction results are obtained after the optimization of the decision-making process. Experiments with different frequency band restrictions show that the δ band signal related to sleep epilepsy affects the prediction performance and the performance of the proposed method is better than the existing sleep epileptic seizure prediction methods.