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

Algorithm for identifying and separating beats from arterial pulse records.

Ernesto F Treo1, Myriam C Herrera, Max E Valentinuzzi

  • 1Departamento de Bioingeniería, Instituto Superior de Investigaciones Biológicas, Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional de Tucumán, Argentina. etreo@herrera.unt.edu.ar

Biomedical Engineering Online
|August 13, 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

Heart failure non-invasive home telemonitoring systems: A systematic review.

Computer methods and programs in biomedicine·2021
Same author

Syncopation and Its Perceptions.

IEEE pulse·2020
Same author

Hearing Aid History: From Ear Trumpets to Digital Technology.

IEEE pulse·2020
Same author

Vaccines and Homeopathy.

IEEE pulse·2020
Same author

Organismic Sets: What Are They?

IEEE pulse·2020
Same author

Tuberculosis, Cholera, Anthrax: Dreadful Culprits.

IEEE pulse·2020
Same journal

Non-invasive classification of stable HFpEF using a deep learning model trained on acoustic features of sustained vowels.

Biomedical engineering online·2026
Same journal

Lung cancer multimodal auxiliary diagnosis based on entropy weight decision fusion.

Biomedical engineering online·2026
Same journal

Potentials of BMSCs for regulating osteogenic-vascular-neural-lymphatic coupling in bone regeneration.

Biomedical engineering online·2026
Same journal

Protein adsorption at material interface: mechanistic design framework for engineering ceramic scaffolds for bone repair applications.

Biomedical engineering online·2026
Same journal

Machine learning models of segmentation in acute ischemic stroke: a systematic review and meta-analysis.

Biomedical engineering online·2026
Same journal

The influence of successful septal myectomy on myocardial stress distributions in the left ventricle: a computational analysis.

Biomedical engineering online·2026
See all related articles

An algorithm for analyzing impedance plethysmography signals accurately identifies heartbeats, outperforming human operators in synchronism and sensitivity for coronary artery disease screening. This tool aids health centers in patient selection.

Area of Science:

  • Biomedical Engineering
  • Cardiovascular Research
  • Medical Informatics

Background:

  • Epidemiological tool for screening coronary artery disease patients.
  • Peripheral artery function assessed non-invasively using impedance plethysmography.
  • Arterial changes predict future coronary events.

Purpose of the Study:

  • Develop an algorithm to identify and separate beats from plethysmographic records.
  • Compare algorithm's beat detection performance against human operators.

Main Methods:

  • Algorithm identifies beats using maximum rising phase, cardiac frequency, and tolerance values.
  • Radial impedance plethysmography and ECG data digitized; cardiac frequency estimated via Power Density Function.
  • Signal processing involved double derivation, binarization, rectification, and filtering to establish beat onsets and ends.

Related Experiment Videos

Main Results:

  • Algorithm demonstrated high sensitivity (97% and 91% for operators vs. algorithm).
  • Accuracy was zero for human operators.
  • Synchronism variability analysis showed the algorithm yielded significantly better results (p < 0.01).

Conclusions:

  • The algorithm exhibits strong performance with high sensitivity for beat detection.
  • Correlation analysis confirms the algorithm's superior synchronism detection.
  • Algorithm is effective for screening, with operator review recommended for patients with arrhythmias.