Singular Spectrum Analysis is a promising computational tool for processing Home Blood Pressure Monitoring data to extract readable trends and long-term variability for clinical prognostics.
The aim is the first application of Singular Spectrum Analysis for computer processing of Home Blood Pressure Monitoring (HBPM). An illustration of the method advantages is the additional objective. The Singular Spectrum Analysis (SSA) is a way of trend and seasonality extraction. SSA is suitable for short series with noises. It is also useful for the variability analysis. As well, it is simply programmable in a high-level programming language. Here Maple 18 was in use. Trends and the slowest oscillations were obtained within a case study. The error of SSA estimations turned out to be close to the exactness of measuring. However, these errors are in good agreement with the known descriptors of the short-time variability. The periods of the slowest oscillations were different for the systolic and the diastolic pressures. Seasonality was about six months and about two months respectively. We found the amplitude of the slowest oscillations of heart rate varies in time. This fact is in contrast to the blood pressure fluctuations, for which the amplitudes are stable. SSA is a promising tool for HBPM data processing. It ensures smooth and readable trends as well as shows the long-term variability of series. This information can be usable for clinical decision-making and prognostics.
No takes yet. Share an insight, caveat, or question.
Chuiko et al. (2017) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: