School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China.
Sci China Life Sci. 2011 Jun;54(6):544-52. doi: 10.1007/s11427-011-4173-y. Epub 2011 Jun 26.
Existing methods of physiological signal analysis based on nonlinear dynamic theories only examine the complexity difference of the signals under a single sampling frequency. We developed a technique to measure the multifractal characteristic parameter intimately associated with physiological activities through a frequency scale factor. This parameter is highly sensitive to physiological and pathological status. Mice received various drugs to imitate different physiological and pathological conditions, and the distributions of mass exponent spectrum curvature with scale factors from the electrocardiogram (ECG) signals of healthy and drug injected mice were determined. Next, we determined the characteristic frequency scope in which the signal was of the highest complexity and most sensitive to impaired cardiac function, and examined the relationships between heart rate, heartbeat dynamic complexity, and sensitive frequency scope of the ECG signal. We found that all animals exhibited a scale factor range in which the absolute magnitudes of ECG mass exponent spectrum curvature achieve the maximum, and this range (or frequency scope) is not changed with calculated data points or maximal coarse-grained scale factor. Further, the heart rate of mice was not necessarily associated with the nonlinear complexity of cardiac dynamics, but closely related to the most sensitive ECG frequency scope determined by characterization of this complex dynamic features for certain heartbeat conditions. Finally, we found that the health status of the hearts of mice was directly related to the heartbeat dynamic complexity, both of which were positively correlated within the scale factor around the extremum region of the multifractal parameter. With increasing heart rate, the sensitive frequency scope increased to a relatively high location. In conclusion, these data provide important theoretical and practical data for the early diagnosis of cardiac disorders.
现有的基于非线性动力学理论的生理信号分析方法仅在单一采样频率下检查信号的复杂性差异。我们开发了一种技术，通过频率尺度因子来测量与生理活动密切相关的多重分形特征参数。该参数对生理和病理状态非常敏感。给小鼠注射各种药物以模拟不同的生理和病理条件，确定来自健康和药物注射小鼠心电图（ECG）信号的质量指数谱曲率随尺度因子的分布。然后，我们确定了信号复杂度最高且对心脏功能障碍最敏感的特征频率范围，并检查了心率、心跳动态复杂性与 ECG 信号敏感频率范围之间的关系。我们发现所有动物都表现出 ECG 质量指数谱曲率绝对值达到最大值的尺度因子范围，该范围（或频率范围）不会随计算数据点或最大粗粒化尺度因子而改变。此外，小鼠的心率不一定与心脏动力学的非线性复杂性相关，而是与通过特定心跳条件下的复杂动态特征的特征描述确定的最敏感的 ECG 频率范围密切相关。最后，我们发现小鼠的心脏健康状况与其心跳动态复杂性直接相关，在多重分形参数极值区域周围的尺度因子内两者呈正相关。随着心率的增加，敏感频率范围增加到相对较高的位置。总之，这些数据为心脏疾病的早期诊断提供了重要的理论和实际数据。
