DocumentCode :
1123547
Title :
Multiple Window Correlation Analysis of HRV Power and Respiratory Frequency
Author :
Hansson-Sandsten, Maria ; Jonsson, Patrik
Author_Institution :
Lund Univ., Lund
Volume :
54
Issue :
10
fYear :
2007
Firstpage :
1770
Lastpage :
1779
Abstract :
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 heart- rate 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 plusmn0.05 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.
Keywords :
correlation methods; electrocardiography; pneumodynamics; spectral analysis; frequency 0.12 Hz to 0.4 Hz; heart rate variability power; mean square error; multiple window correlation analysis; respiratory frequency; Frequency estimation; Frequency measurement; Hafnium; Heart rate; Heart rate variability; Mean square error methods; Narrowband; Power measurement; Psychology; Robustness; Correlation; covariance; heart-rate variability (HRV); multiple-window spectrum analysis; peak-matched multiple windows (PM MWs); respiratory sinus arrhythmia (RSA); Algorithms; Biological Clocks; Computer Simulation; Diagnosis, Computer-Assisted; Heart Rate; Humans; Models, Biological; Respiratory Mechanics; Signal Processing, Computer-Assisted; Statistics as Topic;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/TBME.2007.904527
Filename :
4303263
Link To Document :
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