  Blood-volume-pulse (BVP) is a biosignal commonly used in applications for
non-invasive affect recognition and wearable technology. However, its
predisposition to noise constitutes limitations for its application in
real-life settings. This paper revisits BVP processing and proposes standard
practices for feature extraction from empirical observations of BVP. We propose
a method for improving the use of features in the presence of noise and compare
it to a standard signal processing approach of a 4th order Butterworth bandpass
filter with cut-off frequencies of 1 Hz and 8 Hz. Our method achieves better
results for most time features as well as for a subset of the frequency
features. We find that all but one time feature and around half of the
frequency features perform better when the noisy parts are known (best case).
When the noisy parts are unknown and estimated using a metric of skewness, the
proposed method in general works better or similar to the Butterworth bandpass
filter, but both methods also fail for a subset features. Our results can be
used to select BVP features that are meaningful under different SNR conditions.
