Title :
A wavelet based neural network for prediction of ICP signal
Author :
Tsui, Fu-Chiang ; Sun, Mingui ; Li, Ching-Chung ; Sclabassi, Robert J.
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
Keywords :
backpropagation; biomedical measurement; feedforward neural nets; medical signal processing; patient care; patient monitoring; prediction theory; pressure measurement; recurrent neural nets; signal resolution; wavelet transforms; ICP signal prediction; coarse resolution; coarse scale; computational results; discrete wavelet transform computing wavelet coefficients; dynamic nonlinear models; fine resolution; fine scale; head trauma; intensive care unit; intracranial pressure signal; long-term; multi-step prediction; multiresolution dynamic predictor; recurrent neural networks; short-term; wavelet based neural network; Back; Biological neural networks; Discrete wavelet transforms; Iterative closest point algorithm; Neural networks; Predictive models; Recurrent neural networks; Signal resolution; Wavelet coefficients; Wavelet domain;
Conference_Titel :
Engineering in Medicine and Biology Society, 1995., IEEE 17th Annual Conference
Conference_Location :
Montreal, Que.
Print_ISBN :
0-7803-2475-7
DOI :
10.1109/IEMBS.1995.579462
