A neuro-fuzzy inference system for modeling and prediction of heart rate variability in the neuro-intensive care unit

In the neurological intensive care unit (NICU), prediction of impending changes in patient condition would be highly beneficial. In this paper, we employ a neuro-fuzzy inference system (NFIS) for short-term prediction of heart rate variability in the NICU. An NFIS was selected because it allows for a "gray-box" approach through which a system identification procedure is used in conjunction with fuzzy modeling. The NFIS is described in detail and is compared to an auto-regressive moving average (ARMA) model for its ability to model both simulated and measured data from NICU patients. We found that the NFIS is capable of predicting changes in heart rate to a reasonable extent, and that the NFIS has both advantages and limitations over the ARMA model. The NFIS may therefore be a reasonable technique to consider for more extensive prediction purposes in ICU settings.

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