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Related Concept Videos

Imaging Studies for Cardiovascular System I:Echocardiography01:17

Imaging Studies for Cardiovascular System I:Echocardiography

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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.
Indications: Echocardiography is utilized to diagnose heart failure, valve disorders, and myocardial infarction. It also assesses cardiac structures' size, shape, and motion,...
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Imaging Studies for Cardiovascular System II:Types of Echocardiography01:20

Imaging Studies for Cardiovascular System II:Types of Echocardiography

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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.
Types of Echocardiography
Transthoracic Echocardiography (TTE)
TTE is the most common type of echocardiogram which involves placing a transducer on the patient's chest, emitting sound waves to create heart images. TTE is invaluable for evaluating the heart's size, structure, and motion, making it particularly useful for...
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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

Electrocardiogram

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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.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
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Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

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Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
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Cardiomyopathy II: Dilated Cardiomyopathy01:30

Cardiomyopathy II: Dilated Cardiomyopathy

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Dilated cardiomyopathy, or DCM, is a progressive myocardial disorder characterized by ventricular chamber dilation and contractile dysfunction.EtiologyVarious factors can cause DCM, including hypertension and heavy alcohol intake, which contribute to the weakening and enlargement of the heart muscle. Viral infections, such as Coxsackievirus B, adenoviruses, and influenza, can lead to DCM by causing inflammation and damage to heart tissue. Certain chemotherapeutic agents, including daunorubicin,...
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Related Experiment Video

Updated: Sep 2, 2025

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
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A multimodal parallel method for left ventricular dysfunction identification based on phonocardiogram and

Yajing Zeng1, Siyu Yang2, Xiongkai Yu1

  • 1The Fourth Affiliated Hospital Zhejiang University School of Medicine, Jinhua 321000, China.

Mathematical Biosciences and Engineering : MBE
|August 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for identifying left ventricular dysfunction (LVD) using combined electrocardiogram (ECG) and phonocardiogram (PCG) signals. Fusing these signals improves LVD detection accuracy, aiding early diagnosis and treatment for heart failure patients.

Keywords:
electrocardiogramleft ventricular dysfunctionmulti-modalneural networkphonocardiogram

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence in Medicine

Background:

  • Heart failure (HF) is a critical global health issue, with left ventricular ejection fraction (LVEF) being a key diagnostic and prognostic indicator.
  • Early identification of left ventricular dysfunction (LVD) is crucial for improving patient outcomes and managing HF.
  • Current diagnostic methods may benefit from novel approaches for enhanced accuracy and efficiency.

Purpose of the Study:

  • To develop and validate a novel method for LVD identification using synchronous analysis of electrocardiogram (ECG) and phonocardiogram (PCG) signals.
  • To establish a comprehensive database (SEP-LVDb) for training and testing LVD detection models.
  • To compare the performance of a multimodal approach against single-modality signal analysis.

Main Methods:

  • Creation of the Synchronized ECG and PCG Database for Patients with Left Ventricular Dysfunction (SEP-LVDb) with 1046 recordings.
  • Implementation of a parallel multimodal deep learning model utilizing two-layer bidirectional gate recurrent unit (Bi-GRU) for feature extraction and Residual Network 18 (ResNet-18) for classification.
  • Synchronous analysis of ECG and PCG signals for LVD identification.

Main Results:

  • The fused ECG and PCG signal analysis achieved superior performance compared to using ECG or PCG alone, with an accuracy of 93.27%.
  • Independent dataset verification demonstrated the model's robustness, yielding an accuracy of 80.00%.
  • The Bi-GRU model outperformed other recurrent neural network architectures (Bi-LSTM, RNN), and Saliency Maps confirmed effective feature learning.

Conclusions:

  • Synchronous analysis of ECG and PCG signals offers a promising, accurate, and effective method for identifying left ventricular dysfunction.
  • The developed multimodal deep learning approach holds potential for early LVD detection, contributing to improved heart failure management.
  • This research underscores the value of integrating multiple physiological signals for enhanced diagnostic capabilities in cardiovascular medicine.