Algorithm-supported visual error correction (AVEC) of heart rate measurements in dogs, Canis lupus familiaris

Dog heart rate (HR) is characterized by a respiratory sinus arrhythmia, and therefore makes an automatic algorithm for error correction of HR measurements hard to apply. Here, we present a new method of error correction for HR data collected with the Polar system, including (1) visual inspection of the data, (2) a standardized way to decide with the aid of an algorithm whether or not a value is an outlier (i.e., “error”), and (3) the subsequent removal of this error from the data set. We applied our new error correction method to the HR data of 24 dogs and compared the uncorrected and corrected data, as well as the algorithm-supported visual error correction (AVEC) with the Polar error correction. The results showed that fewer values were identified as errors after AVEC than after the Polar error correction (p < .001). After AVEC, the HR standard deviation and variability (HRV; i.e., RMSSD, pNN50, and SDNN) were significantly greater than after correction by the Polar tool (all p < .001). Furthermore, the HR data strings with deleted values seemed to be closer to the original data than were those with inserted means. We concluded that our method of error correction is more suitable for dog HR and HR variability than is the customized Polar error correction, especially because AVEC decreases the likelihood of Type I errors, preserves the natural variability in HR, and does not lead to a time shift in the data.