Power Spectral Analysis of Short-Term Blood Pressure Recordings for Assessing Daily Variations of Blood Pressure in Human

Hiroyuki Kinoshita, Hiroshi Mannoji, Keita Saku, Jumpei Mano, Tadayoshi Miyamoto, Koji Todaka, Takuya Kishi, Shigehiko Kanaya, Kenji Sunagawa

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this