Journal ArticleDOI
Gaussian Process Robust Regression for Noisy Heart Rate Data
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This paper proposes a robust postprocessing model to infer the latent heart rate time series and applies the method to a wide range of heart rate data and obtains convincing predictions along with uncertainty estimates.Abstract:
Heart rate data collected during nonlaboratory conditions present several data-modeling challenges. First, the noise in such data is often poorly described by a simple Gaussian; it has outliers and errors come in bursts. Second, in large-scale studies the ECG waveform is usually not recorded in full, so one has to deal with missing information. In this paper, we propose a robust postprocessing model for such applications. Our model to infer the latent heart rate time series consists of two main components: unsupervised clustering followed by Bayesian regression. The clustering component uses auxiliary data to learn the structure of outliers and noise bursts. The subsequent Gaussian process regression model uses the cluster assignments as prior information and incorporates expert knowledge about the physiology of the heart. We apply the method to a wide range of heart rate data and obtain convincing predictions along with uncertainty estimates. In a quantitative comparison with existing postprocessing methodology, our model achieves a significant increase in performance.read more
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Textbook Of Work Physiology Physiological Bases Of Exercise
Abstract: Thank you for downloading textbook of work physiology physiological bases of exercise. As you may know, people have look hundreds times for their chosen novels like this textbook of work physiology physiological bases of exercise, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they cope with some harmful virus inside their desktop computer.
Journal ArticleDOI
Assessment of physical activity in youth
TL;DR: This mini-review describes and compares methods to assess habitual physical activity in youth and discusses main issues regarding the use and interpretation of data collected with these techniques.
Journal ArticleDOI
The 'Digital Twin' to enable the vision of precision cardiology.
Jorge Corral-Acero,Francesca Margara,Maciej Marciniak,Cristobal Rodero,Filip Loncaric,Yingjing Feng,Andrew Gilbert,Joao Filipe Fernandes,Hassaan A. Bukhari,Ali Wajdan,Manuel Villegas Martinez,Mariana Sousa Santos,Mehrdad Shamohammdi,Hongxing Luo,Philip Westphal,Paul Leeson,Paolo DiAchille,Viatcheslav Gurev,Manuel Mayr,Liesbet Geris,Pras Pathmanathan,Tina M. Morrison,Richard Cornelussen,Frits W. Prinzen,Tammo Delhaas,Ada Doltra,Marta Sitges,Edward J. Vigmond,Ernesto Zacur,Vicente Grau,Blanca Rodriguez,Espen W. Remme,Steven A. Niederer,Peter Mortier,Kristin McLeod,Mark Potse,Esther Pueyo,Alfonso Bueno-Orovio,Pablo Lamata +38 more
TL;DR: It is argued that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the ‘digital twin’ of a patient.
Journal ArticleDOI
Estimating physical activity energy expenditure, sedentary time, and physical activity intensity by self-report in adults
TL;DR: The RPAQ is the first questionnaire with demonstrated validity for ranking individuals according to their time spent at vigorous-intensity activity and overall energy expenditure.
Proceedings Article
A multivariate timeseries modeling approach to severity of illness assessment and forecasting in ICU with sparse, heterogeneous clinical data
Marzyeh Ghassemi,Marco A. F. Pimentel,Tristan Naumann,Thomas Brennan,David A. Clifton,Peter Szolovits,Mengling Feng +6 more
TL;DR: This work evaluates the use of multivariate timeseries modeling with the multi-task Gaussian process (GP) models using noisy, incomplete, sparse, heterogeneous and unevenly-sampled clinical data, including both physiological signals and clinical notes to assess and forecast patient acuity.
References
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Book
Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Journal ArticleDOI
Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
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A Real-Time QRS Detection Algorithm
Jiapu Pan,Willis J. Tompkins +1 more
TL;DR: A real-time algorithm that reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width of ECG signals and automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate.
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Probability theory : the logic of science
TL;DR: In this article, a survey of elementary applications of probability theory can be found, including the following: 1. Plausible reasoning 2. The quantitative rules 3. Elementary sampling theory 4. Elementary hypothesis testing 5. Queer uses for probability theory 6. Elementary parameter estimation 7. The central, Gaussian or normal distribution 8. Sufficiency, ancillarity, and all that 9. Repetitive experiments, probability and frequency 10. Advanced applications: 11. Discrete prior probabilities, the entropy principle 12. Simple applications of decision theory 15.