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Ambulatory Blood Pressure Signal Features: Identification, Measurement and Implications

  • B. McA. Sayers
  • Loretta R. Cicchiello
Chapter
  • 29 Downloads
Part of the Developments in Cardiovascular Medicine book series (DICM, volume 37)

Abstract

Long term records of blood pressure (mean, systolic or diastolic) and of heart rate are subject to fluctuations that can be regarded as naturally subdividing into five components: a circadian pattern, linear segmental baseline movements, short duration positive or negative unidirectional transients, vasomotor activity and respiratory-linked effects. These components are, in n broad terms, unrelated.

The problems of quantification created by these components is discussed; it is argued that the most appropriate response may be to isolate and measure the components separately and, following up this approach, the contribution of each in short-length sample measurements is assessed.

The similarities between heart rate and beat-variable pressures in respect of certain of these components is considered as a basis for utilising HR measurements for the possible detection of potentially significant occurrences in blood pressure

Keywords

Medical Informatics Residual Signal Phase Spectrum Spectral Average Circadian Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Sayers BMcA. (1973). The analysis of heart rate variability. Ergonomics, 16, 17–32.PubMedCrossRefGoogle Scholar
  2. Sayers, B.McA., Cicchiello L.R., Raftery E.C., Mann S.R. and Green H. (1982) The assessment of continuous ambulatory blood pressure records. Medical Informatics, 7, 93–108.PubMedCrossRefGoogle Scholar
  3. Sayers B.McA., Ruggiero C. and Feuerlicht J. (1981a).Statistical variability of biomedical data: Part 1. The influence of serial correlation on mean value measurements. Medical Informatics, 6, 1–11.PubMedCrossRefGoogle Scholar
  4. Sayers B.McA., Ruggiero C. and Feuerlicht J. (1981b). Statistical variability of biomedical data: Part 2. The influence of serial correlation on power estimates. Medical Informatics, 6, 207–220.PubMedCrossRefGoogle Scholar
  5. Sayers B.McA., Sandoval L.S. and Ruggiero C. (1981c). Statistical sampling strategies for averaging purposes in serially-correlated biomedical data. Medical Informatics, 6, 271–278.PubMedCrossRefGoogle Scholar

Copyright information

© ECSC, EEC, EAEC, Brussels-Luxembourg 1984

Authors and Affiliations

  • B. McA. Sayers
    • 1
  • Loretta R. Cicchiello
    • 2
  1. 1.Imperial CollegeLondonUK
  2. 2.Istituto di Fisica, Facoltà di IngegneriaUniversità di NapoliNapoliItaly

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