A patient-specific neural network model using four photoplethysmography features enabled automated beat-to-beat estimation of systolic and diastolic blood pressure across 670 records from 50 patients.
Does a patient-specific neural network model using PPG features accurately estimate blood pressure in ICU patients?
A patient-specific neural network approach using PPG signals enables accurate, cuff-less beat-to-beat blood pressure estimation without requiring global correction factors.
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 (meanFormula: see textFormula: see textFormula: see textSD) of the estimated SBP as (Formula: see text) mmHg and DBP as (Formula: see text) 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.
Chakraborty et al. (Sat,) conducted a other in Intensive care unit (ICU) patients (n=50). Patient-specific neural network (NN) modeling using PPG features vs. Generalized BP estimation models was evaluated on Accuracy of estimated systolic (SBP) and diastolic (DBP) blood pressure. A patient-specific neural network model using four photoplethysmography features enabled automated beat-to-beat estimation of systolic and diastolic blood pressure across 670 records from 50 patients.