Onset and peak pattern recognition on photoplethysmographic signals using neural networks

Abstract

Traditional methodologies use electrocardiographic (ECG) signals to develop automatic methods for onset and peak detection on the arterial pulse wave. In the present work a Multilayer Perceptron (MLP) neural network is used for classifying fiducial points on photo-plethysmographic (PPG) signals. System was trained with a dataset of temporal segments from signals located based on information about onset and peak points. Different segments sizes and units in the neural network were used for the classification, and optimal values were searched. Results of the classification reach 98.1% in worse of cases. This proposal takes advantages from MLP neural networks for pattern classification. Additionally, the use of ECG signal was avoided in the presented methodology, making the system robust, less expensive and portable in front of this problem. © Springer-Verlag 2013.