PPG-BASED AUTOMATED ESTIMATION OF BLOOD PRESSURE USING PATIENT-SPECIFIC NEURAL NETWORK MODELING.

Recently, photoplethysmography (PPG)-based techniques have been extensively used for cuff-less, automated estimation of blood pressure because of their inexpensive and effortless acquisition technology compared to other conventional approaches. However, most of the reported PPG-based, generalized BP estimation methods often lack the desired accuracy due to pathophysiological diversity. Moreover, some methods rely on several correction factors, which are not globalized yet and require further investigation. In this paper, a simple and automated systolic (SBP) and diastolic (DBP) blood pressure estimation method is proposed based on patient-specific neural network (NN) modeling. Initially, 15 time-plane PPG features are extracted and after feature selection, only four selected features are used in the NN model for beat-to-beat estimation of SBP and DBP, respectively. The proposed technique also presents reasonable accuracy while used for generalized estimation of BP. Performance of the algorithm is evaluated on 670 records of 50 intensive care unit (ICU) patients taken from MIMIC, MIMIC II and MIMIC Challenge databases. The proposed algorithm exhibits high average accuracy with (mean ± SD) of the estimated SBP as (0. 4 6 1 ± 2. 6 2 6) mmHg and DBP as (0. 1 5 0 ± 1. 4 8 2) mmHg. Compared to the other generalized models, the use of patient-specific approach eliminates the necessity of individual correction factors, thus increasing the robustness, accuracy and potential of the method to be implemented in personal healthcare applications. [ABSTRACT FROM AUTHOR]