Hansson-Sandsten Maria, Jönsson Peter
Centre for Mathematical Sciences, Mathematical Statistics, Lund University, Box 118, SE-221 00 Lund, Sweden.
IEEE Trans Biomed Eng. 2007 Oct;54(10):1770-9. doi: 10.1109/TBME.2007.904527.
In this paper, we evaluate the correlation estimate, based on multiple window spectrum analysis, between the respiratory center frequency and the high-frequency band of the heartrate variability (HRV) power. One aim is to examine whether a more restricted frequency range would better capture respiratory related HR variation, especially when the HR variation is changing rapidly. The respiratory peak is detected and a narrow-banded measure of the high-frequency (HF) band of the HRV is defined as the respiratory frequency +/-0.05 Hz. We compare the mean square error of the correlation estimate between the frequency of the respiratory peak and the power of the HRV with the power in the usual 0.12-0.4 Hz frequency band. Different multiple window spectrum techniques are used for the estimation of the respiratory frequency as well as for the power of the HRV. We compare the peak-matched multiple windows with the Welch method while evaluating the two different HF-power estimates mentioned above. The results show that using a more narrow band for the power estimation gives stronger correlation which indicates that the estimate of the power is more robust.
在本文中,我们基于多窗口频谱分析评估呼吸中心频率与心率变异性(HRV)功率高频带之间的相关性估计。一个目的是检验更窄的频率范围是否能更好地捕捉与呼吸相关的心率变化,特别是当心率变化迅速时。检测到呼吸峰值,并将HRV高频(HF)带的窄带测量定义为呼吸频率±0.05Hz。我们将呼吸峰值频率与HRV功率之间相关性估计的均方误差与通常0.12 - 0.4Hz频段的功率进行比较。使用不同的多窗口频谱技术来估计呼吸频率以及HRV的功率。在评估上述两种不同的HF功率估计时,我们将峰值匹配多窗口与韦尔奇方法进行比较。结果表明,使用更窄的频段进行功率估计会产生更强的相关性,这表明功率估计更稳健。