Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Nonlinear statistical modeling and model discovery for cardiorespiratory data.

D G Luchinsky1, M M Millonas, V N Smelyanskiy

  • 1Newstead Mission Critical Technologies, Inc., 9100 Wilshire Boulevard, Suite 540, East Beverly Hills, California 90212-3437, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|October 4, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Complex dynamics and multistability of driven diatomic molecules revealed by numerical modeling of shifted molecular potentials.

Physical review. E·2025
Same author

Suppression and enhancement of vibrational resonance by time-periodic phase modulation in a driven gyroscope.

Physical review. E·2025
Same author

Vibrational resonance in bichromatically excited diatomic molecules in a shifted molecular potential.

Physical review. E·2024
Same author

Crisis-induced vibrational resonance in a phase-modulated periodic structure.

Physical review. E·2024
Same author

Phase coherence-A time-localized approach to studying interactions.

Chaos (Woodbury, N.Y.)·2024
Same author

Effect of a modulated acoustic field on the dynamics of a vibrating charged bubble.

Ultrasonics·2023
Same journal

Tension on dsDNA bound to ssDNA-RecA filaments may play an important role in driving efficient and accurate homology recognition and strand exchange.

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Amplitude-phase coupling drives chimera states in globally coupled laser networks [Phys. Rev. E 91, 040901(R) (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Shapes of sedimenting soft elastic capsules in a viscous fluid [Phys. Rev. E 92, 033003 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Attenuation of excitation decay rate due to collective effect [Phys. Rev. E 90, 022142 (2014)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Role of connectivity and fluctuations in the nucleation of calcium waves in cardiac cells [Phys. Rev. E 92, 052715 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Lattice Boltzmann approach for complex nonequilibrium flows [Phys. Rev. E 92, 043308 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
See all related articles

We developed a Bayesian method to analyze cardiorespiratory (CR) dynamics using blood pressure data. This approach offers an efficient way to understand complex physiological systems without heavy computational needs.

Area of Science:

  • Physiology
  • Biomedical Engineering
  • Computational Biology

Background:

  • Cardiorespiratory (CR) dynamics are crucial for human health.
  • Analyzing these dynamics often requires complex computational models.
  • Blood pressure time-series data contain valuable information about CR interactions.

Purpose of the Study:

  • To present a novel Bayesian dynamical inference method for characterizing human cardiorespiratory dynamics.
  • To enable inverse modeling from blood pressure time-series data.
  • To provide a computationally efficient technique applicable to various dynamical models.

Main Methods:

  • Utilized Bayesian dynamical inference and inverse modeling.
  • Applied the method to blood pressure time-series data.

Related Experiment Videos

  • Developed and validated a simple nonlinear dynamical model for CR dynamics.

    • Main Results:

    • Successfully characterized cardiorespiratory dynamics from blood pressure data.
    • Identified a simple nonlinear dynamical model that accurately describes CR dynamics in the primary frequency band.
    • Demonstrated the method's accuracy using simulated data.

    Conclusions:

    • The presented Bayesian method offers an efficient and accurate approach for analyzing cardiorespiratory dynamics.
    • The technique is versatile, applicable to various stochastic dynamical models.
    • The inferred model shows connections to established baroreflex models, enhancing physiological understanding.