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Related Concept Videos

Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
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Electrocardiogram01:29

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An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
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Imaging Studies for Cardiovascular System I:Echocardiography01:17

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Cardiac imaging studies encompass a wide range of noninvasive and minimally invasive techniques designed to visualize the heart's structure and function in detail. One such technique is echocardiography, which uses high-frequency ultrasound waves to produce detailed images of the heart, known as echocardiograms.
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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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Imaging Studies for Cardiovascular System II:Types of Echocardiography01:20

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Echocardiography plays a role in assessing cardiac health and detecting heart conditions, with various types providing critical insights for diagnosis and treatment.
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Bode Plots Construction01:24

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The Bode plot is an essential tool in control system analysis, mapping the frequency response of a system through a magnitude plot and a phase plot, both against a logarithmic frequency axis. To construct a Bode plot, consider the transfer function H(ω):
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Related Experiment Video

Updated: Jan 18, 2026

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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Bimodal ECG and PCG Cardiovascular Disease Detection: Exploring the Potential and Modality Contribution.

Alessia Calzoni1,2, Mattia Savardi3, Marco Silvestri4

  • 1University of Brescia, Department of Information Engineering, Via Branze 38, Brescia, 25123, Italy. alessia.calzoni@unibs.it.

Journal of Medical Systems
|September 12, 2025
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Summary

This study introduces a novel deep learning model combining electrocardiogram (ECG) and phonocardiogram (PCG) signals for earlier cardiovascular disease (CVD) detection. The bimodal approach significantly improves diagnostic accuracy compared to single-modality methods.

Keywords:
Bimodal learningCardiovascular diseasesElectrocardiogramExplainabilityMultimodal fusionPhonocardiogramTransfer learning

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Cardiology

Background:

  • Early detection of cardiovascular diseases (CVDs) is vital for patient outcomes and reducing healthcare costs.
  • Electrocardiograms (ECGs) and phonocardiograms (PCGs) are cost-effective, non-invasive tools for CVD screening.
  • Limited availability of bimodal (ECG+PCG) datasets hinders the development of advanced diagnostic models.

Purpose of the Study:

  • To develop and evaluate a novel bimodal deep learning model integrating ECG and PCG signals for enhanced early CVD detection.
  • To address the challenge of limited bimodal data by leveraging pre-trained models and publicly available unimodal datasets.
  • To interpret the model's decision-making process and visualize feature separation between normal and pathological samples.

Main Methods:

  • A bimodal deep learning architecture was proposed, featuring a late fusion of a fine-tuned audio-pre-trained CNN (for PCG) and a 1D-CNN (for ECG).
  • The PCG branch was fine-tuned using all available unimodal PCG datasets.
  • The model was evaluated on an augmented version of the MITHSDB dataset, employing explainability techniques and UMAP for visualization.

Main Results:

  • The bimodal model achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 96.4%, outperforming ECG-only (approx. 93.4%) and PCG-only (approx. 85.4%) models.
  • Explainability methods quantified the contributions of electrical and acoustic features.
  • UMAP visualization demonstrated clear separation between normal and pathological cardiac samples.

Conclusions:

  • Combining ECG and PCG signals via a bimodal deep learning approach significantly enhances early CVD detection accuracy.
  • Explainability and visualization techniques provide valuable insights into the model's performance and feature contributions.
  • The study highlights the potential of multimodal data fusion for CVD diagnosis and emphasizes the need for larger, diverse bimodal datasets.