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

ECG Data-Acquisition and classification system by using wavelet-domain Hidden Markov Models.

Pedro R Gomes1, Filomena O Soares, J H Correia

  • 1Faculty of Engineering and Technologies of University Lusiada, Largo Tinoco de Sousa, 4760-108 V. N. Famalicao Portugal. pedroreis.soares@dei.uminho.pt

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 25, 2010
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

Schoolchildren's exposure to potentially toxic metals/metalloids and cognitive impairments in communities of the Brazilian Amazon new agricultural frontiers.

Neurotoxicology·2026
Same author

Raman spectroscopy for classification of neoplastic and non-neoplastic CAM colon tumors.

Heliyon·2024
Same author

Highly-selective optical filter for NADH fluorescence detection in multiphoton microscopy.

Biomedical optics express·2024
Same author

Applying genomic approaches to delineate conservation strategies using the freshwater mussel Margaritifera margaritifera in the Iberian Peninsula as a model.

Scientific reports·2022
Same author

Design, simulation, and fabrication of an ingestible capsule with gastric balloon for obesity treatment.

Biomedical physics & engineering express·2021
Same author

Task-Based Automatic Evaluation of People with Intellectual Disabilities Performed on a Robotic Table Soccer.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2020
Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
See all related articles

This study classifies electrocardiogram (ECG) pulses using Continuous Density Hidden Markov Models (CDHMMs) and Wavelet Transform (WT). The method accurately identifies various heart rhythms, aiding in arrhythmia detection and physician analysis.

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Cardiology

Background:

  • Accurate classification of electrocardiogram (ECG) pulses is crucial for diagnosing cardiac arrhythmias.
  • Existing methods may require complex computational resources or lack robustness in real-world data.
  • The need for reliable, low-cost systems for continuous cardiac monitoring is growing.

Purpose of the Study:

  • To develop and evaluate a state-of-the-art classification system for ECG pulses.
  • To utilize Continuous Density Hidden Markov Models (CDHMMs) combined with Wavelet Transform (WT) for enhanced ECG analysis.
  • To identify and differentiate between normal (N), premature ventricular contraction (V), supra-ventricular arrhythmia (S), atrial fibrillation (AF), and atrial flutter (AFL) heartbeats.

Main Methods:

Related Experiment Videos

  • Simultaneous observation of ECG signals at three levels of focus using Wavelet Transform (WT).
  • Application of Continuous Density Hidden Markov Models (CDHMMs) for beat classification.
  • Utilizing both MLII and V1 ECG derivations for comprehensive signal capture.
  • Detection of runtime classification errors through differing derivation classifications.

Main Results:

  • Successful classification of various ECG pulse types including normal, premature ventricular contraction, atrial fibrillation, and atrial flutter.
  • Demonstrated effectiveness on real-world data from the MIT-BIH Arrhythmia Database.
  • Validation using data from a developed low-cost Data-Acquisition System.

Conclusions:

  • The CDHMM and WT approach provides a robust method for ECG pulse classification.
  • The system can detect runtime classification errors, enhancing reliability for physician analysis.
  • This technology offers a promising avenue for improved cardiac arrhythmia detection and monitoring.