Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

You might also read

Related Articles

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

Sort by

Same author

Variant in a Taste Receptor Locus Tied to Changes in the Use of Insomnia Medication.

Biological psychiatry global open science·2026

Same author

Observational and Mendelian randomization studies of plasma sclerostin levels do not provide evidence of cardiovascular adverse effects of sclerostin inhibition.

Human molecular genetics·2025

Same author

An Open-Source Framework for Virtual Bioequivalence Modeling and Clinical Trial Design.

CPT: pharmacometrics & systems pharmacology·2025

Same author

Rare loss-of-function variants in HECTD2 and AKAP11 confer risk of bipolar disorder.

Nature genetics·2025

Same author

A bivariate multifractal analysis approach to understanding socio-spatial segregation dynamics.

Scientific reports·2025

Same author

Sequence variants associated with BMI affect disease risk through BMI itself.

Nature communications·2024

Search research articles

Related Experiment Video

Updated: May 25, 2026

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

Framework for adaptive multiscale analysis of nonhomogeneous point processes.

Hannes Helgason¹, Jay Bartroff, Patrice Abry

¹SIP Laboratory, KTH, Stockholm, Sweden. hannes.helgason@ee.kth.se

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 19, 2012

Summary

This summary is machine-generated.

We developed methods for hypothesis testing and model selection in non-constant rate heart beat data using nonhomogeneous Poisson processes. Our approach accurately models variability and selects the best model from various templates.

More Related Videos

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Published on: February 8, 2014

Related Experiment Videos

Last Updated: May 25, 2026

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Published on: February 8, 2014

Area of Science:

Statistics
Biomedical Engineering
Computational Biology

Background:

Heartbeat data analysis requires modeling complex, non-constant rate processes.
Detecting variability in physiological signals is crucial for understanding health and disease.

Purpose of the Study:

To develop a robust methodology for hypothesis testing and model selection in nonhomogeneous Poisson processes.
To apply these methods for modeling and variability detection in heart beat data.

Main Methods:

Modeling non-constant rate functions using basis function templates.
Developing a generalized likelihood ratio statistic for template selection.
Utilizing a dynamic programming algorithm for multiscale template computation.

Main Results:

The proposed procedure demonstrates high power in hypothesis testing and model selection.
Numerical examples show performance comparable to super-optimal methods.
The methodology effectively models and detects variability in heart beat data.

Conclusions:

The developed methods provide an effective framework for analyzing nonhomogeneous Poisson processes, particularly in biomedical applications.
The approach offers a powerful tool for heart beat data modeling and variability detection.
Potential for extension to more general history-dependent point processes.