Epileptic seizure prediction based on multivariate statistical process control of HRV
2017-03-06 18:18
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提取的RR间期信息：
时域特征： 均值，标准差，相邻RR间期差值的平方均值，总能量， 固定时间内相邻 RR间期差值大于50ms的次数
频域特征: 利用傅里叶变换，计算低频带能量，高频带能量，低频带和高频带能量之比。
输入特征：8维。 3min的滑动窗。
算法：Multivariate Statistical Process Control
14个病人， 24-72小时不等
18.9h 正常期间RR 间期信息用来建模。其余用来验证和测试。
38.4h正常心跳。如果在癫痫发作15min之前就已经预测到，认为预测成功
结果：召回率91%，
时域特征： 均值，标准差，相邻RR间期差值的平方均值，总能量， 固定时间内相邻 RR间期差值大于50ms的次数
频域特征: 利用傅里叶变换，计算低频带能量，高频带能量，低频带和高频带能量之比。
输入特征：8维。 3min的滑动窗。
算法：Multivariate Statistical Process Control
14个病人， 24-72小时不等
18.9h 正常期间RR 间期信息用来建模。其余用来验证和测试。
38.4h正常心跳。如果在癫痫发作15min之前就已经预测到，认为预测成功
结果：召回率91%，
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