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

Search research articles

Related Experiment Videos

Modeling of cardiac rhythms. A signal-processing perspective.

P C Doerschuk¹, T M Chin, A S Willsky

¹Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge 02139.

Journal of Electrocardiology

|January 1, 1990

Summary

This study explores modeling cardiac rhythms for arrhythmia signal processing. Statistical models guide algorithm development, but approximations are key for computational feasibility.

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

Phase I and biomarker study of OPB-51602, a novel signal transducer and activator of transcription (STAT) 3 inhibitor, in patients with refractory solid malignancies.

Annals of oncology : official journal of the European Society for Medical Oncology·2015

Same author

The positive impact of cytological specimens for EGFR mutation testing in non-small cell lung cancer: a single South East Asian laboratory's analysis of 670 cases.

Cytopathology : official journal of the British Society for Clinical Cytology·2012

Same author

3-D image reconstruction from averaged Fourier transform magnitude by parameter estimation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008

Same author

A multiscale hypothesis testing approach to anomaly detection and localization from noisy tomographic data.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008

Same author

Image segmentation and edge enhancement with stabilized inverse diffusion equations.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008

Same author

Multiscale methods for the segmentation and reconstruction of signals and images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008

Area of Science:

Biomedical Engineering
Signal Processing
Computational Cardiology

Background:

Cardiac rhythm modeling is crucial for developing effective arrhythmia signal-processing algorithms.
Existing models often capture only aspects relevant to specific signal-processing tasks.
Statistical approaches offer a framework for defining optimal algorithms based on cardiac behavior.

Purpose of the Study:

To present a perspective on modeling cardiac rhythms for signal processing.
To highlight the importance of purpose-driven model selection in algorithm development.
To discuss the role of statistical methods and mathematical formulations in optimizing signal processing.

Main Methods:

Utilizing statistical descriptions of cardiac behavior relevant to signal processing goals.

Related Experiment Videos

Investigating two mathematical formulations: stochastic Petri nets and interacting Markov chains.

Analyzing how different model forms influence the development of approximations for computational efficiency.

Main Results:

Statistical modeling, when combined with performance criteria, can specify optimal signal-processing algorithms.
Optimal algorithms derived from statistical models are frequently computationally intractable for real-time applications.
The choice of mathematical model formulation significantly impacts the nature of feasible approximations.

Conclusions:

Approximations are essential for implementing computationally intensive signal-processing algorithms in real-time systems.
Stochastic Petri nets and interacting Markov chains offer distinct frameworks for developing approximately optimal algorithms.
The mathematical form of cardiac rhythm models is critical for deriving practical and efficient signal-processing solutions.