Quantification of respiratory sinus arrhythmia using the IPANEMA body sensor network

In clinical practice the determination of the heart rate variability (HRV) has become a common measure to investigate the parasympathetic cardiac control. Especially the measurement of the respiratory sinus arrhythmia (RSA) has gained importance to asses the HRV. The RSA can be seen as an indirect parameter for the physiological or psychological stress the patient is currently exposed to. Thus, this parameter is used to identify specific characteristics of disease in a broad field of clinical disciplines. In this contribution, we present a BSN-based approach of assessing the RSA in a long-term evaluation. For this purpose, we use two sensor types: A three channel ECG sensor node which was introduced before and a recently developed respiratory sensor based on conductive yarn. We further implemented an oscillatory model-based Unscented Kalman filter (UKF) to estimate the heart rate as well as the breathing rate and, thus, to calculate the RSA. The algorithm is finally validated by performing deep breathing tests (DBT) on a healthy test subject in order to force an increased occurrence of the RSA. The results of the developed system and proposed algorithm are finally discussed with respect to its applicability in different every days situations.