Linear interpolation is a computationally simpler and practically equivalent alternative to low pass filtering for transforming evoked heart rate and blood pressure data into equidistant time series.
May simplify preprocessing of evoked cardiovascular signals in research; leaves open validation in diverse psychophysiological datasets.
For many years psychophysiologists have been interested in stimulus related changes in heart rate and blood pressure. To represent these evoked heart rate and blood pressure patterns, heart rate and blood pressure data have to be transformed into equidistant time series. This paper presents an extensive comparison between two methods. The most often used method is based on linear interpolation, also known as weighted averaging. The low pass filtering method presented here is based on a well-known model for the generation of heart beats, the integral pulse frequency modulation model (IPFM). The comparison shows that the results of the filtering and interpolation procedures are virtually identical. Practically, small differences between the methods disappear in the averaging process. Therefore, the interpolation method is a suitable practical alternative to the computationally complex filtering method.
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Koers et al. (1999) studied this question.
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