A wavelet based neural network for prediction of ICP signal

Abstract

We present a wavelet-based neural network for multi-step prediction of the intracranial pressure (ICP) signal. A multiresolution dynamic predictor (MDP) is proposed, which utilizes the discrete wavelet transform computing wavelet coefficients from coarse scale to fine scale and recurrent neural networks (RNNs) forming dynamic nonlinear models for prediction. It has the ability to predict the ICP in both long-term with coarse resolution and short-term with fine resolution. Computational results up to three scale levels have demonstrated the effectiveness of the MDP for multi-step prediction as compared with the the raw data.