August 20, 2008IEEE Transactions on Biomedical Engineering
Gaussian Process Robust Regression for Noisy Heart Rate Data
Population
Heart rate data collected during nonlaboratory conditions
Comparison
Robust postprocessing model consisting of… vs Existing postprocessing methodology
Design
Other
Authors
Discussion
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Member takes
Implication
May improve ambulatory HR monitoring; leaves open prospective validation before clinical use.
Structured PICO
P
Population
Heart rate data collected during nonlaboratory conditions
I
Intervention
Robust postprocessing model consisting of unsupervised clustering followed by Bayesian (Gaussian process) regression
C
Comparator
Existing postprocessing methodology
O
Outcome
Prediction performance of latent heart rate time series
A novel Gaussian process robust regression model improves the processing and prediction of noisy heart rate data collected in nonlaboratory settings.
Cite This Study
Stegle et al. (2008) studied this question.