Abstract
Advances in monitoring technology allow blood pressure waveforms to be collected at sampling frequencies of 250–1000 Hz for long time periods. However, much of the raw data are under-analysed. Heart rate variability (HRV) methods, in which beat-to-beat interval lengths are extracted and analysed, have been extensively studied. However, this approach discards the majority of the raw data. Objective: Our aim is to detect changes in the shape of the waveform in long streams of blood pressure data. Approach: Our approach involves extracting key features from large complex data sets by generating a reconstructed attractor in a three-dimensional phase space using delay coordinates from a window of the entire raw waveform data. The naturally occurring baseline variation is removed by projecting the attractor onto a plane from which new quantitative measures are obtained. The time window is moved through the data to give a collection of signals which relate to various aspects of the waveform shape. Main results: This approach enables visualisation and quantification of changes in the waveform shape and has been applied to blood pressure data collected from conscious unrestrained mice and to human blood pressure data. The interpretation of the attractor measures is aided by the analysis of simple artificial waveforms. Significance: We have developed and analysed a new method for analysing blood pressure data that uses all of the waveform data and hence can detect changes in the waveform shape that HRV methods cannot, which is confirmed with an example, and hence our method goes ‘beyond HRV’.
Highlights
The cardiovascular system keeps blood in continuous motion around the body ensuring that adequate cellular oxygen and nutrient requirements are met at any given time
When the blood pressure data are viewed over a long time interval, the individual oscillations can no longer be distinguished and the only information that is readily observable is the vertical motion of the average blood pressure which will vary depending on whether the animal is resting, active, eating, sleeping, etc
The only preprocessing that we perform on the data is the simple removal of any obvious outliers that are outside a specified range. These typically arise from electrical interference which results in spikes in the data. This is in contrast to the preparation of data for Heart rate variability (HRV) analysis in which the heart beats have to be identified in the data in order to find the beat-to-beat (RR) intervals
Summary
The cardiovascular system keeps blood in continuous motion around the body ensuring that adequate cellular oxygen and nutrient requirements are met at any given time. Whilst a number of approaches have been taken to quantify and interpret the variability of cardiovascular signals, the associated physiological relevance of these measures has been the subject of much debate and there is still disagreement around this [41] Much attention in this area has focussed on heart rate variability (HRV) which considers variability in the beat-to-beat intervals, which are influenced by both the sympathetic and parasympathetic nervous systems, as well as many other factors [25]. We are able to limit the introduction of inadvertent bias and to represent the gradients and contours of the waveform in a manner which allows us to extract extra information from the input signal This may provide a deeper understanding of physiological or pathological changes within the cardiovascular system that may be missed when focussing on maxima, minima and interval data alone. All the proofs of the various results stated in the paper are presented in the Appendix
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