Extraction of direct respiratory influences form the tachogram using multiscale principal component analysis

Heart rate variability (HRV) studies are widely used to assess the functioning of the autonomic nervous system. Due to respiratory influences in the tachogram, the interpretation of HRV measures is questioned. This paper addresses this issue by estimating the respiratory component from the tachogram using multiscale principal component analysis (MSPCA), a technique that combines wavelet analysis with principal component analysis. Subsequently, the extracted respiratory component is subtracted from the tachogram in order to obtain a tachogram without respiratory influences. The results show that initial significant correlations and coherences between the tachogram and respiration are no longer present after the application of the proposed method, demonstrating that the direct, linear influence of respiration is reduced.