In this paper, we consider some different aspects involved in the prediction of biological time series. This is often a difficult task, which can be accomplished by defining a suited function approximation problem whose inputs are determined using past samples of the sequence to be predicted. We demonstrate that neural networks can be very useful in this regard. For instance, we illustrate how Mixture of Gaussian neural networks can be efficiently used for the prediction of glucose temporal evolution in Diabetes Mellitus Type 2, instead of using the well-known MINMOD model. We also focus on the chaotic nature of biological time series, which can be contaminated by spurious noise. Neural networks represent a powerful tool for the prediction of these sequences, when the past samples to be used for prediction are chosen by suitable embedding techniques. A new embedding approach is proposed in the paper, using a genetic algorithm where each individual represents a possible embedding solution. We demonstrate that the proposed technique yields better prediction accuracies, considering as a particular application the prediction of those biological parameters (glucose, heart rate, and skin conductivity) that are strictly related to road safety, since they may evidence stress conditions or possible loss of consciousness. (C) 2010 Elsevier Ltd. All rights reserved.

Advances in biological time series prediction by neural networks / Panella, Massimo. - In: BIOMEDICAL SIGNAL PROCESSING AND CONTROL. - ISSN 1746-8094. - STAMPA. - 6:2(2011), pp. 112-120. ((Intervento presentato al convegno 1st International Symposium on Bioelectronics and Bioinformatics (ISBB2009) tenutosi a Melbourne, AUSTRALIA nel DEC, 2009 [10.1016/j.bspc.2010.09.006].

Advances in biological time series prediction by neural networks

PANELLA, Massimo
2011

Abstract

In this paper, we consider some different aspects involved in the prediction of biological time series. This is often a difficult task, which can be accomplished by defining a suited function approximation problem whose inputs are determined using past samples of the sequence to be predicted. We demonstrate that neural networks can be very useful in this regard. For instance, we illustrate how Mixture of Gaussian neural networks can be efficiently used for the prediction of glucose temporal evolution in Diabetes Mellitus Type 2, instead of using the well-known MINMOD model. We also focus on the chaotic nature of biological time series, which can be contaminated by spurious noise. Neural networks represent a powerful tool for the prediction of these sequences, when the past samples to be used for prediction are chosen by suitable embedding techniques. A new embedding approach is proposed in the paper, using a genetic algorithm where each individual represents a possible embedding solution. We demonstrate that the proposed technique yields better prediction accuracies, considering as a particular application the prediction of those biological parameters (glucose, heart rate, and skin conductivity) that are strictly related to road safety, since they may evidence stress conditions or possible loss of consciousness. (C) 2010 Elsevier Ltd. All rights reserved.
biological time series; embedding technique; genetic algorithm; glucose evolution; neural networks; prediction
01 Pubblicazione su rivista::01a Articolo in rivista
Advances in biological time series prediction by neural networks / Panella, Massimo. - In: BIOMEDICAL SIGNAL PROCESSING AND CONTROL. - ISSN 1746-8094. - STAMPA. - 6:2(2011), pp. 112-120. ((Intervento presentato al convegno 1st International Symposium on Bioelectronics and Bioinformatics (ISBB2009) tenutosi a Melbourne, AUSTRALIA nel DEC, 2009 [10.1016/j.bspc.2010.09.006].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/112112
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