基于多域特征和GATv2网络的癫痫发作预测方法
韩哲,孟庆芳,张强,张相龙,赵亚欧

Seizure prediction method based on multi-domain feature and GATv2 network
Zhe HAN,Qingfang MENG,Qiang ZHANG,Xianglong ZHANG,Yaou ZHAO
表 1 时域的统计特征
Tab.1 Statistical characteristics of time domain
特征描述
最大值$ {X}_{\text{Max}} $=max$ \{{x}_{1},{x}_{2},\cdots, {x}_{n}\} $
最小值$ {X}_{\text{Min}} $=min$ \{{x}_{1},{x}_{2},\cdots, {x}_{n}\} $
中值奇数时为中间元素,偶数时为中间2个元素和取均值
众数频率最高的值
方差$ {X}_{\text{Var}} $=$ \dfrac{1}{N}\displaystyle\sum\nolimits_{n=1}^{N}{\left(\overline{X}-{{X}_{n}}\right)}^{2} $
均值$ \overline{X} $=$ \dfrac{1}{N}\displaystyle\sum\nolimits_{n=1}^{N}{X}_{n} $
标准差$ \sigma $=$ \sqrt{\dfrac{1}{N}\displaystyle\sum\nolimits_{n=1}^{N}{\left(\overline{X}-{{X}_{n}}\right)}^{2}} $
取值范围$ {X}_{\text{Ra}} = {X}_{\text{Max}} $$- {X}_{\text{Min}} $
四分位距$ {I}_{\text{QR}} = {Q}_{3}- {Q}_{1} $
偏度$ {X}_{\text{Ske}} = \dfrac{1}{N}\displaystyle\sum\nolimits_{i=1}^{N}\dfrac{{\left(x_i-\overline{x}\right)}^{3}}{{\sigma }^{3}} $
峰度$ {X}_{\text{kurt}} $=$ \dfrac{1}{N}\displaystyle\sum\nolimits_{i=1}^{N}\dfrac{{\left(x_i-\overline{x}\right)}^{4}}{{\sigma }^{4}} $−3
波动系数${\mathrm{CV}}= \dfrac{\sigma }{\overline{x}} $
分形维数$ D=\underset{\epsilon \rightarrow 0}{\lim } \dfrac{\lg \;({N}(\epsilon))}{\lg \;(1/ \epsilon)} $
样本熵$ \mathrm{SampEn}\;(m,r)=-\ln \left[\dfrac{{B}^{m+1}(r)}{{B}^{m}(r)}\right] $
排列熵$ {H}_{\text{pe}}({P}_{j})=-\displaystyle\sum\limits_{{j}=1}^{{k}}{P}_{j}\ln ({P}_{j}) $
模糊熵$ \text{FuzzyEn}\left(m,r\right)=-\ln \left(\dfrac{A(m,r)}{B(m,r)}\right) $