心动周期信号中独立成分的分离

采用一项新的统计信号处理技术——独立成分分析提取心动周期信号子成分。从 10名受试者的仰卧和站立体位分别采集 8m in心电信号 ,进而提取心动周期信号。按时间延迟把心动周期信号分成 5组 ,进行独立成分分析 ,重构出两组成分。两组成分分别经傅立叶变换 ,结果显示一组信号成分集中于低频区域 (称为 IC1) ,另一组集中于高频区域 (称为 IC2 )。从仰卧位到站位 IC1的功率显著增加 (P<0 .0 1) ,IC2的功率没有显著的变化 (P>0 .0 5 ) ,IC1的功率占总功率的比值显著增加 (P<0 .0 1)。两体位的比较研究结果表明采用独立成分分析心动周期信号得到的两组成分中 ,IC1可以表征交感神经系统的活动 ,而 IC2表征了副交感神经系统的活动。由这些成分得到的数字和图谱信息可以分别定量评价交感和副交感神经系统的功能

Separating Independent Components in Heart Period Signal

To extract sub-signal of heart period signal (HPS), a new statistical signal processing approach, namely independent component analysis (ICA) was addressed. Electrocardiosignal (ECS) was acquired from ten volunteers. ECS was sampled 8 minutes when the volunteer was in supine position, and then when the same volunteer was in erect position. HPS was extracted from ECS. According to time-delay, HPS was divided into five groups as mixed signals. Five signals were reconstructed into two groups by ICA. The rebuilt signals were transformed by Fourier transformation. One centralized in low frequency (called IC1); the other did in high frequency (called IC2). The power of IC1 was significantly increased (P<0.01) while that of IC2 showed no significant change (P>0.05), and the ratio of IC1 to total power also significantly increased with the change from supine position to erect position. Comparsion between the two postural results reveals that IC1 may express sympathetic activity, and IC2 represents parasympathetic activity. Sympathetic and parasympathetic nervous functions can be evaluated respectively and quantitatively by use of data and graphs from the two decomposed components.