We developed an artificial-neural-network-based adaptive filter (ANNADF) for nonlinear biomedical signal filtering and modeling. We addressed the issues of (1) the stability condition, (2) convergence rate, (3) generalization capability for noise elimination, and (4) the sensitivity towards weight error of the ANNADF. We tested the performance of the ANNADF for simulated linear and nonlinear signals and sampled biomedical signals. Based on the ANNADF, we developed an ANN-based adaptive matched filter for QRS detection, and an ANN-based multichannel adaptive filter for evoked potential signal enhancement. All the results were compared with those of linear filters, and the comparison results show than ANN-based filters outperform linear filters for nonlinear biomedical signal processing applications. We also proposed several methods to reduce the excessive number of neurons and synaptic weights in a feedforward, multi-layer perceptron artificial neural network. These methods were applied to several typical classification problems, as well as ECG pattern classification and nonlinear mapping of speech modeling patterns. Results show that this approach offers a potentially systematic tool to determine the number of hidden units of feedforward ANN models, and thus, to improve the efficiency of pattern recognition by ANN models.
展开▼
