Comparison of MLP and REN classifiers for detection of hemodynamic stress using photoplethysmograph

Abstract

The aim of this study is to show a comparison of the Multi-Layered Perceptron (MLP) neural network and Recurrent Elman Network (REN) in determining false positives for Pulse Photoplethysmogram (PPG) recorded during rest and recovery phase after exercise. Several time domain features, depicting the signal morphology and time indices were identified for classification and the robustness of the neural networks were examined using Classification Accuracy (CA) and Receiver Operating Characteristics (ROC). The obtained CA’s were 91% (MLP) and 96.75% (REN) for Reflection Index (RI) and 90% (MLP) and 94% (REN) for Stiffness Index (SI). Besides, the REN is slightly better with a ROC index of 0.94485 and 0.96742 for RI and SI. From the obtained results, it can be concluded that the REN was found to detect lower false positives when compared to the MLP network.