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

Authors: Natarajan Sriraam; Paruthi Pradhapan; Muthukaruppan Swaminathan; Hari Krishna Salila Vijayalal Mohan

Addresses: Center for Medical Electronics and Computing, M.S. Ramaiah Institute of Technology, Bangalore, Karnataka 560054, India ' Department of Electronics and Communication Engineering, Tampere University of Technology, Korkeakoulunkatu 10, FI-33720 Tampere, Finland ' Temasek Life Sciences Laboratory Limited, 1 Research Link, National University of Singapore, 117604, Singapore ' School of Mechanical and Aerospace Engineering, Nanyang Technological University, Micromachines Lab 1, 50 Nanyang Avenue, N3.1-B3Mb-07, 639798, Singapore

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.

Keywords: multi-layered perceptron; MLP; recurrent Elman networks; REN; pulse photoplethysmograms; PPG; cardiovascular disorders; neural networks; receiver operating characteristics; classification accuracy; time domain; false positives; hemodynamic stress; rest and recovery; exercise; heart rate.

DOI: 10.1504/IJBET.2013.056287

International Journal of Biomedical Engineering and Technology, 2013 Vol.12 No.1, pp.97 - 112

Received: 31 Oct 2012
Accepted: 25 Jun 2013
Published online: 27 Sep 2014 *

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