基于机器学习的跨患者癫痫自动检测算法

针对目前癫痫自动检测算法多集中于为单个患者建立检测模型, 泛化能力较弱的问题, 提出一种基于机器学习的跨患者癫痫自动检测算法. 该算法使用多个癫痫患者的脑电数据, 先对数据进行预处理后分析脑电数据间存在的特征, 再对特征进行筛选, 训练出一个跨患者的癫痫自动检测模型. 该算法不需为每个患者 建立单独的检测模型, 实现了仅使用一个检测模型即可对不同患者进行癫痫检测. 实验结果准确率为0.877 4, 敏感性为0.854 8, 特异性为0.9.

Automatic Epileptic Seizure Detection Algorithm for Non-specific Patient Based on Machine Learning

Aiming at the problem that the automatic epileptic seizure detection algorithm focused on the establishment of a detection model for single patient, and the generalization ability was weak, we proposed an automatic epileptic seizure detection algorithm for non-specific patient based on machine learning. The algorithm used the electroencephalography (EEG) data of multiple epileptic patients, analyzed the characteristics of EEG data after preprocessing the data, and then selected the characteristics to train an automatic epileptic seizure detection model for non-specific patients. The algorithm did not need to establish a separate detection model for each patient, it could detect epilepsy in different patients with only one detection model. The accuracy, sensitivity and specificity of the algorithm are 0.877 4,0.854 8 and 0.9, respectively.