Davide Fontana
Predicting Cardiovascular Risk with Machine Learning: A Data-Driven Analysis of Blood Pressure and Heart Rate Variability.
Rel. Danilo Demarchi, Irene Buraioli. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2024
Abstract
Blood pressure (BP) is a key vital parameter in assessing cardiovascular diseases. It undergoes continuous dynamic fluctuations across different timescales, spanning durations from seconds to years, driven by a complex interaction of environmental and emotional factors along with cardiovascular regulatory mechanisms. Blood pressure variability (BPV) is defined by the magnitude and patterns of BP fluctuations, quantified through various indices that capture different aspects of BP dynamics. These indices include dispersion measures, which assess the overall spread of blood pressure values; sequence indices, that evaluate patterns of change over time; instability measures, that indicate the variability in blood pressure readings; behavioural indices, that consider factors such as daily activities and stress; frequency domain analyses, that decompose BP fluctuations into their constituent frequencies.
Nevertheless, the clinical significance of BPV has not been fully determined, especially when using spot clinic measurements and including diastolic blood pressure in its calculation
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