ID 原文 译文
40416 发现Ksd=0.3可作为区分AF和NSR的重要参数。 From the experiment, Ksd=0.3 can be an important parameter to distinguish AF from NSR.
40417 本研究不仅能以阴阳特性描述的方式区分出AF和NSR，也为第四统计力学的探索打开了新途径，并且对未来准确快速检测AF提供重要依据，对AF早期干预与改善患者预后至关重要。 This study can not only distinguish AF and NSR by describing Yin and Yang, but also open a new path for exploration of the fourth statistical theory and provide an important evidence for the accurate and rapid detection of AF in the future.
40418 针对现有心电信号(Electrocardiogram，ECG)去噪方法难以精准剥离与之频带重叠的肌电干扰并无损提取到“干净”ECG的问题，提出了利用心动周期和经验模式分解对含噪ECG进行去噪处理。 In order to solve the problem that the existing electrocardiogram(ECG)denoising methods are difficult to accurately separate the overlapped myoelectricity interference and extract“clean”ECG, this paper proposes a method of using cardiac cycle and empirical mode decomposition to denoise the noisy ECG signal.
40419 本文方法首先对含噪ECG进行经验模式分解，然后利用心动周期判断固有模态函数分量属于噪声还是有用信号， Firstly, empirical mode decomposition is used to decompose the noisy ECG signal, and then the intrinsic modal function components of the signal are determined to be noise or useful signal by cardiac cycle.
40420 最后将有用信号的固有模态函数分量重构ECG。 Finally, the intrinsic modal function components of the useful signal are reconstructed to be ECG signal.
40421 为验证本文去噪方法，首先采用ECG动力学仿真模型评估本文方法在不同参数噪声下的去噪效果； For validating the proposed method, the dynamic simulation model of ECG signal is used to evaluate the denoising effect of the method under different parameters of noise;
40422 其次选取MIT-BIH数据库中的基线漂移信号bw，肌电干扰信号ma和105号、107号、123号ECG分别构建3组真实含噪ECG进行实验。 and three groups of real noisy ECG are constructed by selecting baseline drift signal bw, myoelectricity interference signal ma and ECG105, 107 and 123 in MIT-BIH database, respectively.
40423 评估与实验结果均表明本文方法可以简单、有效地同时去除ECG中的肌电干扰和基线漂移，去噪效果优于普通经验法。 Both the evaluation and experimental results show that the method can remove the myoelectricity interference and baseline drift in ECG at the same time, and the denoising effect is better than the usual empirical method.
40424 提取颈部肌肉的肌音(Mechanomyography，MMG)信号时域、时-频域和非线性动力学的15个常见特征， Fifteen typical features in time domain, time-frequency domain and non-linear dynamic are extracted from the mechanomyogarphy(MMG)signals in neck muscles.
40425 按照其性质分为5个特征集，并选择其中一部分构建高维特征矢量后进行主成分分析(Principal component analysis，PCA)降维处理，应用于头部动作的模式识别研究中。 They are divided into five feature sets according to their nature, and part of them are constructed to high-dimension feature vectors before reducing the dimension by principal component analysis(PCA), which are applied in the pattern research for head movements.