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Due to the peculiarities of dog HR variability, an automatic algorithm may wrongly delete values as outliers, which are part of the dog HR pattern, which was the case when using the Polar system tool for the error correction.
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By indexing tables and reasoning from deleted values, we show that STR3 can avoid systematically iterating over the full set of current tuples, contrary to STR2.
Filtering was performed by deleting values that were outside the physiologically feasible range.
When individual items were deleted values above 0.85 were obtained in all sum scores.
Subjects with no deleted values were then removed from the analysis since there is no missing value to impute.
The pNN50 changed by approximately 2 % when replaced by means, whereas after deleting values, the change was always below 2%%.
Furthermore, the HR data strings with deleted values seemed to be closer to the original data than were those with inserted means.
At Day 1, the internal consistency among the items (Cronbach's Alpha) was 0.87; when individual constituent items were deleted values ranged from 0.85 to 0.87, indicating that no further item reduction was required.
In contrast, particularly with HRV, deleting values may not only influence further analysis toward more conservative results, but may also shift it qualitatively, mainly because it can disturb the basic time series that is required for HRV analysis (Berntson & Stowell, 1998).
As a result the procedure described in this article may result in incorrectly deleting values from the data.
Cofactors were selected by stepwise regression according to Miller [ 24], pp. 49] with an "F-to-enter" and "F-to-delete" value of 3.5.
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