An Independent Component Analysis Based Estimation of Ambulatory Blood Pressure from the Radial Pulse Waveform

Continuous ambulatory blood pressure (ABP) records may be an important independent determinant to the better prevention and diagnosis of cardiovascular disease. The purpose of this study is to develop and validate a novel application of independent component analysis (ICA) based auto-regression forecasting model (ICA-ARF) to noninvasively, continuously and conveniently derive ABP from the radial artery pressure waveform (RAPWF) in humans. RAPWF is acquired using a pressure transducer worn on the wrist, so it may perturbed by many factors, such as the tightness of wrist strap, the location of sensor and so on. To eliminate the effect of correlativity among measurement factors, ICA method is used to decompose the raw signal and extract the independent component which is affected by blood pressure (BP) only. Based on the spectrum density of independent component, an auto-regression forecasting model is set up to derive BR An experiment to validate the prediction model is done and the results show an excellent correlation and agreement between directly measured BP which is measured by Omron electronic BP monitor (Type: HEM-7012). When the ICA-ARF is used, the error reduces from 7.4 mmHg to 2.55 mmHg. The novel methods have important implications for the simplification of noninvasive CASP measurement and 24-hour ambulatory BP monitoring and assessment of central aortic blood pressure.