Heart rate variation adaptive filtering based on a special model

Abstract

The low- and high-frequency components of an RR heart rate signal must be adequately separated to provide accurate heart rate variability indices of sympathetic and parasympathetic activity. Adaptive filters can separate the low-frequency sympathetic and high-frequency parasympathetic components from an ECG RR interval signal, enabling the attainment of more accurate heart rate variability measures. For the raised case, this chapter suggests an efficient (short size) case-based model and illustrates its performance in adaptive filtering of heart rate signal. This method renders analogous results to what a higher order conventional FIR model adaptive filter may yield. The advantage of this model lays in its ability to accommodate the phase difference between breathing signal and the HF component of HRV using a low-order tunable filter. Simulation results supporting the proposed scheme are presented.