Gaussian Process Robust Regression for Noisy Heart Rate Data

By Oliver Stegle, Sebastian V. Fallert, David John Cameron MacKay, Søren Brage. 2008

230 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Barbara E. Engelhardt
2. Li-Fang Cheng
3. Michael Draugelis
4. Corey J. Chivers
5. Marzyeh Ghassemi
6. Peter Szolovits
7. Marco A. F. Pimentel
8. David A. Clifton

In Cardiology and Cardiovascular Medicine

1. Robert Dürichen
2. Marzyeh Ghassemi
3. David A. Clifton

Citations in the corpus

• A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units (2017, methodology): “One commonly used approach to resolve missing and irregularly sampled time series data is Gaussian processes (GPs, [Stegle et al., 2008, Dürichen et al., 2015, Ghassemi et al., 2015]).”
• Sparse Multi-Output Gaussian Processes for Medical Time Series Prediction (2017, methodology): “When modeling medical time series, the SE kernel or the periodic kernel are often used in combination to capture the unknown source-specific smoothness and periodicity of the trajectories of clinical covariates (Stegle et al., 2008; Dürichen et al., 2015).”
• The ‘Digital Twin’ to enable the vision of precision cardiology (2020, unknown): “23 There are clinical needs that can be solved with a single modelling approach.”
• Multitask Gaussian Processes for Multivariate Physiological Time-Series Analysis (2015, methodology): “[5] used GPs for a robust regression of noisy heart rate data and Dürichen et al.”
• A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment and Forecasting in ICU with Sparse, Heterogeneous Clinical Data (2015, methodology): “Similarly, GPs were used for robust regression of noisy heart rate data (Stegle et al. 2008).”