Fig 1.
Distributions (kernel density estimation) for each of the twelve features, per label (high or low heart rate).
The distributions are computed over all chunks for all participants, thus 1120 data points per feature (513 low, 607 high). Orange and blue values indicate median and standard deviation of each of the high and low heart rate distributions, respectively.
Table 1.
Outcomes (R2) of the linear regression models, per pre-processing approach.
Features were either derived directly from the pre-processing approach, or with added second-degree polynomials for each feature. Models were fit to the 80% train set and evaluated on the 20% test set.
Table 2.
AUCs of the model pre-selection process (averaged over 50 independent model runs).
Table 3.
AUCs and parameters of the best-performing models and runner-up models resulting from hyperparameter search (on the averaging pre-processing approach).
Fig 2.
Mean (± 95% CI) feature importance’s as extracted from the best-performing model of each of the 50 runs.
Higher values imply a higher degree of information within the variable. The vertical dashed line represents the overall mean of all importance values. The asterisks represent where feature importance’s differed significantly from the overall mean.