Comparison of Artificial Intelligence Based Oscillometric Blood Pressure Estimation Techniques: A Review Paper

Accurate Blood Pressure (BP) measurement is an important physiological health parameter in the field of health monitoring, which is significant in determining the cardiovascular health of the patient under observation. Nowadays, automated blood pressure measurement systems are generally used by patients at home, and this requires less expertise to operate. The major requirement in the design of Automated Blood Pressure (ABP) measurement systems is the degree of accuracy and repeatability. There are various Artificial Intelligence (AI) based blood pressure estimation techniques and algorithms developed by various researchers in recent years and some of them are commonly employed by the BP monitoring market in the design of their automated blood pressure systems for accurate estimation of patient’s systolic and diastolic blood pressures. In this review paper, various AI based Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) estimation techniques and algorithms are analyzed and compared in terms of their ability for accurate estimation of real time patient blood pressure. The performance of various AI based blood estimation techniques are analyzed in terms of their complexity, Mean Absolute Error (MAE) and Standard Deviation Error (SDE).

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