A Modeling Approach for Coefficient-Free Oscillometric Blood Pressure Estimation

Oscillometry is the most common measurement method used in automatic blood pressure (BP) monitors. However, most of the oscillometric algorithms are without physiological and theoretical foundation, and rely on empirically derived coefficients for systolic and diastolic pressure evaluation which affects the reliability of the technique. In this thesis, the oscillometric BP estimation problem is addressed using a comprehensive modeling approach, based on which coefficient-free estimation of BP becomes possible. A feature-based neural network approach is developed to find an implicit relationship between BP and the oscillometric waveform (OMW). The modeling approach is then extended by developing a mathematical model for the OMW as a function of the arterial blood pressure, cuff pressure, and cuff-arm-artery system parameters. Based on the developed model, the explicit relationship between the OMW and the systolic and diastolic pressures is found and a new coefficient-free oscillometric BP estimation method using the trust region reflective algorithm is proposed. In order to improve the reliability of BP estimates, the electrocardiogram signal is recorded simultaneously with the OMW, as another independent source of information. The electrocardiogram signal is used to identify the true oscillometric pulses and calculate the pulse transit time (PTT). By combining our developed model of oscillomtery with an existing model of the pulse wave velocity, a new mathematical model is derived for the PTT during the cuff deflation. The derived model is incorporated to study the PTT-cuff pressure dependence, based on which a new coefficient-free BP estimation method is proposed. In order to obtain accurate and robust estimates of BP, the proposed model-based BP estimation method sare fused by computing the weighted arithmetic mean of their estimates. With fusion of the proposed methods, it is observed that the mean absolute error (MAE) in estimation of systolic and diastolic pressures is 4.40 and 3.00 mmHg, respectively, relative to the Food and Drug Administration-approved Omron monitor. In addition, the proposed feature-based neural network was compared with auscultatory measurements by trained observers giving MAE of 6.28 and 5.73 mmHg in estimation of systolic and diastolic pressures, respectively. The proposed models thus show promise toward developing robust BP estimation methods