التفاصيل البيبلوغرافية
| العنوان: |
Bayesian at heart: towards autonomic outflow estimation via generative state-space modelling of heart rate dynamics |
| المؤلفون: |
Fernando Ernesto Rosas De Andraca, Diego Candia-Rivera, Andrea I Luppi, Yike Guo, Pedro A M Mediano |
| سنة النشر: |
2024 |
| المجموعة: |
University of Sussex (US): Figshare |
| مصطلحات موضوعية: |
Bioinformatics and computational biology, Biomedical and clinical sciences, Engineering, Health services and systems, Information and computing sciences, Applied computing, Autonomic nervous system, Bayesian statistics, Heart rate, Non-linear methods, State-space modelling, Bayes Theorem, Heart, Brain, 46 Information and Computing Sciences, 4601 Applied Computing, Cardiovascular, Neurosciences, 08 Information and Computing Sciences, 09 Engineering, 11 Medical and Health Sciences, Biomedical Engineering, 3102 Bioinformatics and computational biology, 4203 Health services and systems |
| الوصف: |
Recent research is revealing how cognitive processes are supported by a complex interplay between the brain and the rest of the body, which can be investigated by the analysis of physiological features such as breathing rhythms, heart rate, and skin conductance. Heart rate dynamics are of particular interest as they provide a way to track the sympathetic and parasympathetic outflow from the autonomic nervous system, which is known to play a key role in modulating attention, memory, decision-making, and emotional processing. However, extracting useful information from heartbeats about the autonomic outflow is still challenging due to the noisy estimates that result from standard signal-processing methods. To advance this state of affairs, we propose a novel approach in how to conceptualise and model heart rate: instead of being a mere summary of the observed inter-beat intervals, we introduce a modelling framework that views heart rate as a hidden stochastic process that drives the observed heartbeats. Moreover, by leveraging the rich literature of state-space modelling and Bayesian inference, our proposed framework delivers a description of heart rate dynamics that is not a point estimate but a posterior distribution of a generative model. We illustrate the capabilities of our method by showing that it recapitulates linear properties of conventional heart rate estimators, while exhibiting a better discriminative power for metrics of dynamical complexity compared across different physiological states. |
| نوع الوثيقة: |
article in journal/newspaper |
| اللغة: |
unknown |
| العلاقة: |
10779/uos.25487446.v1; https://figshare.com/articles/journal_contribution/Bayesian_at_heart_towards_autonomic_outflow_estimation_via_generative_state-space_modelling_of_heart_rate_dynamics/25487446Test |
| الإتاحة: |
https://figshare.com/articles/journal_contribution/Bayesian_at_heart_towards_autonomic_outflow_estimation_via_generative_state-space_modelling_of_heart_rate_dynamics/25487446Test |
| حقوق: |
CC BY 4.0 |
| رقم الانضمام: |
edsbas.BD658CB8 |
| قاعدة البيانات: |
BASE |
