Abstract:

In order to analyze arrhythmia, a noval practical method was proposed. Here the pRRx serials (x ranged from 1 to 100 ms) extracted from ECG data were taken as fundamental signals. There were obvious differences in distribution of the pRRx serials between 20 people with normal arrhythmia (group I) and 20 patients with arrhythmia (group II). By analyzing the linear indexes and nonlinear indexes of pRRx serials, the computed results show that there are significant statistical differences between the two groups. The linear indexes (AVRR, rMSSD, SDSD) are very different (P<0.001). The nonlinear indexes, from the entropy measures (S_dh, S_ph, S_pf) and the fractal dimension measures (D_sf, D_cf, D_vm, D_rms), also maintain apparent differences (P<0.001). Therefore, the proposed pRRx-serial analysis can characterize the linearity and nonlinearity of the cardiac system to some extent, and can be effective in recognizing the arrhythmia and even heart-related diseases.

Key words: ECG, pRRx serials, linear indexes, nonlinear indexes, entropy, fractal dimension, arrhythmia

摘要：

提出一种从ECG数据中提取pRRx序列, 进而分析心律失常的方法。选取20例正常窦性心律和20例心律失常患者的ECG数据, 计算相应的pRRx序列(x从1~100 ms取值), 两组pRRx序列的分布呈现明显的差异。对pRRx序列进行线性和非线性分析, 结果表明: 1) 线性指标中, AVRR, rMSSD和SDSD在两组序列中表现出显著性差异(P<0.001); 2) 非线性指标中, pRRx序列直方分布信息熵(S_dh)、功率谱直方分布信息熵(S_ph)功率谱全频段信息熵(S_pf)和pRRx的分形维数(D_sf, D_cf, D_vm和D_rms)在两组序列中表现出显著的统计学差异(P<0.001)。因此, 基于pRRx序列的分析在一定程度上能够反映心脏系统的线性和非线性性质, 可以作为一种新颖且有效的心律失常分析方法。

关键词: ECG, pRRx序列, 线性指标, 非线性指标, 熵, 分形维数, 心律失常