An ensemble artificial neural network model using photoplethysmography signals produced a lower mean absolute error for predicting intermittent blood pressure compared to the best single ANN model.
Does an ensemble artificial neural network model improve the prediction of intermittent blood pressure from PPG signals compared to a single ANN model?
An ensemble artificial neural network model using multiscale entropy features from PPG signals improves the accuracy of intermittent blood pressure prediction compared to a single ANN model.
This study evaluates the correlation between the intermittent blood pressure (BP) and the photoplethysmography (PPG). This study of a total of twenty-five cases is started by the partitioning of the PPG signal into a 5-minute segment. The segmented PPG is filtered by ensemble empirical mode decomposition (EEMD). The feature extraction method, multiscale entropy (MSE) is utilized for the purified signal to achieve some information. The seventy-five scale of MSE is taken into the input of the artificial neural network (ANN) modeling. The outputs of this system are the intermittent diastolic and systolic blood pressure. Originally, thousand models are created. The best model is chosen for the best single ANN model. In advanced, the ensemble artificial neural network (EANN) model is initiated to observe the testing data. The result, compared to the best single ANN model, shows that the EANN model recognizes better the testing data by producing lower mean absolute error (MAE).
Sadrawi et al. (Thu,) conducted a other in Blood pressure prediction (n=25). Ensemble artificial neural network (EANN) model vs. Best single ANN model was evaluated on Mean absolute error (MAE) for intermittent diastolic and systolic blood pressure. An ensemble artificial neural network model using photoplethysmography signals produced a lower mean absolute error for predicting intermittent blood pressure compared to the best single ANN model.
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