Heart rate variability (HRV) has been proposed as an indicator of stress. However, respiratory changes affect the spectral content of the HRV, resulting in a misleading estimation of stress, especially when the respiratory rate falls into the classical low frequency band. To overcome this limitation of the classical HRV analysis, this study decomposes the HRV signal, recorded during different phases of acute emotional stress, into two components using orthogonal subspace projections (OSP). One component describes all linear respiratory influences, and the other one contains all residual HRV dynamics. Two subspace definitions are compared here, on the one hand, the original respiration signal, and on the other hand, its wavelet decomposition. After a multicomparison test, no difference was found between the respiratory components derived using both subspaces, hence, no added value is achieved by the wavelet decomposition. Furthermore, the HRV variations that are linearly related to respiration are significantly different (p andlt; 0.008) between relax and emotional stress. This suggests that respiratory dynamics are enough to detect emotional stress, which might result in an improved assessment of stress.
