Multilayer Perceptrons for Time Series Prediction: A Case Study on Heart Signals

The study of the dynamicity of the response of the heart ventricles to external stimuli is of major interest to assess the risk of sudden death. The given task is to predict the changes of the so called QT duration in function of the instantaneous changes of the RR interval. The QT interval measures the duration of activation and inactivation of the heart ventricles while the RR interval represents the heart rate. These two intervals are measured on the body-surface Electrocardiogram (ECG). In this paper multilayer perceptrons (MLP) are used to create predictive models of the QT-RR relationship. It’s however difficult to obtain good quality signals covering all possible values of RR and QT, making the choice of the learning set a major challenge. Therefore, in addition to real data, simulated data are used for the design of the MLPs and the assessment of their performances. Learning and predicting the simulated data allowed to understand the generalization behavior of MLPs outside learning zones. These data also permitted to test the predictive quality of MLP trained on real signals allowing, in case of differences between predicted QTs and measured ones, to understand if the differences are due to a model dysfunction or a physiological phenomenon.