Zhang Tinglin, Wang You, Li Guang
State Key Lab of Industrial Control Technology, Zhejiang University, 38 Zheda Road, Hangzhou 310027, PR China; Department of Control Science and Engineering, Zhejiang University, 38 Zheda Road, Hangzhou 310027, PR China.
State Key Lab of Industrial Control Technology, Zhejiang University, 38 Zheda Road, Hangzhou 310027, PR China; Department of Control Science and Engineering, Zhejiang University, 38 Zheda Road, Hangzhou 310027, PR China.
Neurosci Lett. 2016 Mar 23;617:39-45. doi: 10.1016/j.neulet.2016.01.063. Epub 2016 Feb 2.
A single-channel algorithm was proposed in order to study effect of intermittent hypoxic training on hypoxia tolerance based on EEG pattern. EEG was decomposed by ensemble empirical mode decomposition into a finite number of intrinsic mode functions (IMFs) based on the intrinsic local characteristic time scale. Analytic amplitude, analytic frequency, and recurrence property quantified by recurrence quantification analysis were explored on IMFs, and the first two scales revealed difference between normal EEG and hypoxia EEG. Classification accuracy of hypoxia EEG and normal EEG could reach 67.8% before decline of neurobehavioral ability, which represented that hypoxia EEG pattern could be detected at an early stage. Classification accuracy of hypoxia EEG and normal EEG increased with time and deepened intensity of hypoxia was observed by regular shift of hypoxia EEG pattern with time in a three dimensional subspace. The reduced shift and classification accuracy after intermittent hypoxic training represented that hypoxia tolerance enhanced.
为了基于脑电图模式研究间歇性低氧训练对低氧耐受性的影响，提出了一种单通道算法。脑电图通过总体经验模式分解，基于内在局部特征时间尺度分解为有限数量的固有模式函数（IMF）。对IMF探索了通过递归量化分析量化的解析幅度、解析频率和递归特性，前两个尺度揭示了正常脑电图和低氧脑电图之间的差异。在神经行为能力下降之前，低氧脑电图和正常脑电图的分类准确率可达67.8%，这表明低氧脑电图模式可以在早期被检测到。低氧脑电图和正常脑电图的分类准确率随时间增加，并且通过低氧脑电图模式在三维子空间中随时间的规律变化观察到低氧强度加深。间歇性低氧训练后变化和分类准确率降低表明低氧耐受性增强。
