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Nonlinear filtering of oscillatory measurements in cardiovascular applications. (English) Zbl 1189.92065

Summary: An array of nonidentical and locally connected chaotic biological neurons is modelled by a single representative chaotic neuron model based on an extension of the Hindmarsh-Rose neuron. This model is then employed in conjunction with the unscented Kalman filter to study the associated state estimation problem. The archetypal system, which was deliberately chosen to be chaotic, was corrupted with noise. The influence of noise seemed to annihilate the chaotic behaviour. Consequently it was observed that the filter performs quite well in reconstructing the states of the system although the introduction of relatively low noise had a profound effect on the system. Neither the noise-corrupted process model nor the filter gave any indications of chaos. We believe that this behaviour can be generalised and expect that unscented Kalman filtering of the states of a biological neuron is completely feasible even when the uncorrupted process model exhibits chaos. Finally the methodology of the unscented Kalman filter is applied to filter a typical simulated ECG signal using a synthetic model-based approach.

MSC:

92C55 Biomedical imaging and signal processing
92C20 Neural biology
62M20 Inference from stochastic processes and prediction
92C50 Medical applications (general)
37D45 Strange attractors, chaotic dynamics of systems with hyperbolic behavior
37N25 Dynamical systems in biology
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References:

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