Adaptive filtering for respiration influence reduction on Heart Rate Variability

In this paper it is described an adaptive method for Heart Rate Variability (HRV) signal filtering, which uses a noise canceller structure formed by a Finite Impulse Response (FIR) filter together with the Least Mean Squares (LMS) adaptation algorithm in order to reduce respiration influence on HRV information. Respiration and electrocardiogram (ECG) signals were obtained simultaneously using 240Hz sampling frequency during 5-minutes experiments. Respiration signal was acquired by mechanic methods whereas ECG signal was obtained using one lead electrocardiograph. After data acquisition, a tachogram was derived from ECG measurement in order to obtain the HRV signal; then Adaptive Noise Cancelling (ANC) filtering was applied, reducing artifacts due to respiration from HRV signal. This method was evaluated for spontaneous and controlled respiration frequency by comparing results from the Power Spectral Density (PSD) of HRV signal before and after filtering. At the results, it is observed that frequency components related to respiration are cancelled in the HRVs PSD, reaching an appropriate estimation of the control exerted by the Autonomic Nervous System (ANS) in the cardiac activity.