Open Science Research Excellence
%0 Journal Article
%A Desmond B. Keenan and  Paul Grossman
%D 2008 
%J  International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering
%B World Academy of Science, Engineering and Technology
%I International Science Index 19, 2008
%T Adaptive Filtering of Heart Rate Signals for an Improved Measure of Cardiac Autonomic Control
%U http://waset.org/publications/11142
%V 19
%X In order to provide accurate heart rate variability
indices of sympathetic and parasympathetic activity, the low
frequency and high frequency components of an RR heart rate signal
must be adequately separated. This is not always possible by just
applying spectral analysis, as power from the high and low frequency
components often leak into their adjacent bands. Furthermore,
without the respiratory spectra it is not obvious that the low
frequency component is not another respiratory component, which
can appear in the lower band. This paper describes an adaptive filter,
which aids the separation of the low frequency sympathetic and high
frequency parasympathetic components from an ECG R-R interval
signal, enabling the attainment of more accurate heart rate variability
measures. The algorithm is applied to simulated signals and heart rate
and respiratory signals acquired from an ambulatory monitor
incorporating single lead ECG and inductive plethysmography
sensors embedded in a garment. The results show an improvement
over standard heart rate variability spectral measurements.
%P 232 - 238