Artificial Neural Networks estimating blood pressure from PPG signals showed better accuracy than linear regression, satisfying the American National Standards of the AAMI.
Does an Artificial Neural Network-based method improve the accuracy of continuous blood pressure estimation from a PPG signal compared to linear regression?
An Artificial Neural Network-based method can accurately estimate continuous blood pressure from PPG signals, outperforming linear regression and meeting AAMI standards.
There is a relation, not always linear, between the blood pressure and the pulse duration, obtained from photoplethysmography (PPG) signal. In order to estimate the blood pressure from the PPG signal, in this paper the Artificial Neural Networks (ANNs) are used. Training data were extracted from the Multiparameter Intelligent Monitoring in Intensive Care waveform database for better representation of possible pulse and pressure variation. In total there were analyzed more than 15000 heartbeats and 21 parameters were extracted from each of them that define the input vector for the ANN. The comparison between estimated and reference values shows better accuracy than the linear regression method and satisfy the American National Standards of the Association for the Advancement of Medical Instrumentation.
Kurylyak et al. (Wed,) conducted a other in Blood pressure estimation. Artificial Neural Networks (ANNs) vs. Linear regression method was evaluated on Accuracy of blood pressure estimation compared to reference values. Artificial Neural Networks estimating blood pressure from PPG signals showed better accuracy than linear regression, satisfying the American National Standards of the AAMI.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: