Azami Hamed, Smith Keith, Fernandez Alberto, Escudero Javier
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7422-5. doi: 10.1109/EMBC.2015.7320107.
Alzheimer's disease (AD) is one of the fastest growing neurological diseases in the world. We evaluate multivariate multiscale sample entropy (mvMSE) and multivariate multiscale permutation entropy (mvMPE) approaches to distinguish resting-state magnetoencephalogram (MEG) signals of 36 AD patients from those of 26 normal controls. We also discuss about choosing the appropriate embedding dimension value as an effective parameter for mvMPE and MPE for the first time. The results illustrate that both the mvMPE and mvMSE can be useful in the diagnosis of AD, although with different running times and abilities. In addition, our findings show that the MEG complexity analysis performed on deeper time scales by mvMPE and mvMSE may be a useful tool to characterize AD. In most scale factors, the average of the mvMPE and mvMSE values of AD patients are lower than those of controls.
阿尔茨海默病（AD）是全球增长速度最快的神经疾病之一。我们评估了多变量多尺度样本熵（mvMSE）和多变量多尺度排列熵（mvMPE）方法，以区分36例AD患者与26例正常对照者的静息态脑磁图（MEG）信号。我们还首次讨论了选择合适的嵌入维数值作为mvMPE和MPE的有效参数。结果表明，mvMPE和mvMSE在AD诊断中均有用，尽管运行时间和能力不同。此外，我们的研究结果表明，通过mvMPE和mvMSE在更深时间尺度上进行的MEG复杂性分析可能是表征AD的有用工具。在大多数尺度因子下，AD患者的mvMPE和mvMSE值的平均值低于对照组。
