基于多尺度的人体ECG信号质量指数谱分析

Mass exponent spectrum analysis of human ECG signals based on multiple scale factors

提出一种多重分形质量指数谱曲率和面积的概念,Cantor集的验证结果证明该两个参数在探测混沌时间序列复杂性方面是完全有效的.引入多尺度的分析方法,详细研究了不同采样频率和数据点下健康人、心肌缺血患者和心梗患者心电图(ECG)信号的质量指数谱,并与其他非线性参数进行了比较,期望从不同个体间的这种差异,获得区分健康人及有疾病人的非线性特征值.分类指标和单因素方差分析(ANOVA)检验结果表明,该方法达到了最好的综合区分效果.这一结论在早期诊断和临床应用方面具有一定的价值.

Life is one of the most complex nonlinear systems and heart is the core of this lifecycle system. The complexity of electrocardiogram （ECG） signals may reflect the physiologic function and health status of the heart. In this paper, we introduced two novel parameters of the muhifractal mass exponent spectrum curvature and area. The evaluation of Cantor set validated that the two indicators are entirely effective in exploring the complexity of chaotic time series. Using the muhiscale analysis method, we studied the mass exponent spectra of ECG signals taken from the cohorts of healthy, ischemia and myocardial infarction （MI） sufferer under different sampling frequencies and data lengths. Then we compared these new indicators with other nonlinear parameters and also expected to acquire some valuable nonlinear eigenvalues to distinguish the healthy from the heart diseased through those individual discrepancies. The classification indexes and ANOVA testing results both indicated that our method＇ could achieve better results. These conclusions may be of much value in early diagnoses and clinical applications.