Abstract
Although daily variations of blood pressure (BP) predict cardiovascular event risk, their assessment requires ambulatory BP monitoring which hinders the clinical application of this approach. Since the baroreflex is a major determinant of BP variations, especially in the frequency range of 0.01-0.1 Hz (baro-frequency), we hypothesized that the power spectral density (PSD) of short-term BP recordings in the baro-frequency range may predict daily variations of BP. In nine-week-old Wister-Kyoto male rats (N =5) with or without baroreflex dysfunction, we telemetrically recorded continuous BP for 24 hours and estimated PSD using Welch's periodogram for the recordings during the 12-hour light period. We compared the reference PSD of 12-hour recording with the PSDs obtained from shorter data lengths ranging from 5 to 240 minutes. The 30-minute BP recordings reproduced PSD of 12-hour recordingswell, and PSD in the baro-frequency range paralleled the standard deviation of 12-hour BP. Thus, the PSD of 30-minute BP reflects the daily BP variability in rats. In human subjects, we estimated PSD from 30-minute noninvasive continuous BP recordings. The rat and human PSDs shared remarkably similar characteristics. Furthermore, comparison of PSD between elderly and young subjects suggested that the baro-frequency range in humans overlapped with that in rats. In conclusion, PSD derived from 30-minute BP recordings is capable of predicting daily BP variations. Our proposed method may serve as a simple, noninvasive and practical tool for predicting cardiovascular events in the clinical setting.
Original language | English |
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Pages (from-to) | 1-4 |
Number of pages | 4 |
Journal | Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference |
Volume | 2018 |
DOIs | |
Publication status | Published - Jul 1 2018 |
All Science Journal Classification (ASJC) codes
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics