Methods for Non-invasive Trustworthy Estimation of Arterial Blood Pressure

• Main Author: Koohi, Iraj
• Other Authors: Groza, Voicu
• Language: en
• Published: Université d'Ottawa / University of Ottawa 2017
• Subjects: oscillometric; trustworthiness; dynamic threshold; trusted boundaries; compliance parameter; regression model
• Online Access: http://hdl.handle.net/10393/35830; http://dx.doi.org/10.20381/ruor-20113

The trustworthiness of the blood pressure (BP) readings acquired by oscillometric home-based monitoring systems is a challenging issue that requires patients to see the doctor for trusted measurements, especially those who are obese or have cardiovascular diseases such as hypertension or atrial fibrillation. Even with the most accurate monitors one may get different readings if BP is repeatedly measured. Trusted BP readings are those measured with accurate devices at proper measurement conditions. The accurate monitors need an indicator to assure the trustworthiness of the measured BP. In this work, a novel algorithm called the Dynamic Threshold Algorithm (DTA) is proposed that calculates trusted boundaries of the measured systolic and diastolic pressures from the recorded oscillometric waveforms. The DTA determines a threshold from the heart rate of subjects to locate the oscillometric pulse at the mean arterial pressure (PULSEMAP) and uses the peak, trough, and pressure of the located pulse to calculate the trusted boundaries. In terms of accuracy, a modeling approach is employed to estimate BP from the arterial lumen area oscillations model in the diastolic region (ALA-based). The model requires compliance parameter ‘c’ to estimate BP. To this end, a pre-developed linear regression model between ‘c’ and the corresponding amplitude ratio of the PULSEMAP is employed to evaluate ‘c’. The proposed method uses ‘c’ and estimates BP by minimizing differences between peak and trough amplitudes of the actual and corresponding simulated waveforms. The proposed DTA and ALA-based methods were tested on two datasets of healthy subjects and one dataset of sick subjects with cardiovascular diseases, and results were validated against corresponding references and compared with two popular maximum amplitude and maximum/minimum slope algorithms. Mean absolute error (MAE) and standard deviation of errors (STDE) are used to evaluate and compare the results. For healthy subjects, the MAE of the estimated systolic (SBP) and diastolic (DBP) blood pressures was improved up to 57% and 57% with an STDE of 55% and 62%, respectively. For sick subjects, the MAE was improved up to 40% and 29% with an STDE of 36% and 20% for SBP and DBP, respectively.