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Related Concept Videos

Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Dysrhythmias I: Introduction01:15

Dysrhythmias I: Introduction

Dysrhythmias refers to abnormalities in the heart's rhythm. They result from disruptions in the heart's electrical conduction system, which includes the sinoatrial(SA)node, atrioventricular(AV) node, the bundle of His, bundle branches, and Purkinje fibers.Definition and PathophysiologyDysrhythmias result from disorders of impulse formation, impulse conduction, or both. The heart contains specialized cells in the sinoatrial node, atrioventricular node, and the bundle of His and Purkinje fibers...
Dysrhythmias II: Classification of Tachyarrhythmias01:28

Dysrhythmias II: Classification of Tachyarrhythmias

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Related Experiment Video

Updated: Jul 19, 2026

Behavioral Determination of Stimulus Pair Discrimination of Auditory Acoustic and Electrical Stimuli Using a Classical Conditioning and Heart-rate Approach
10:50

Behavioral Determination of Stimulus Pair Discrimination of Auditory Acoustic and Electrical Stimuli Using a Classical Conditioning and Heart-rate Approach

Published on: June 6, 2012

Mamba-Based Prototypical Contrastive Learning With Augmented Feature Separation for Common and Rare Arrhythmia

Fengyi Guo, Ying An, Jianxin Wang

    IEEE Journal of Biomedical and Health Informatics
    |March 3, 2026
    PubMed
    Summary

    This study introduces a Mamba-based framework for diagnosing rare arrhythmias using electrocardiograms (ECGs). The approach enhances early detection of cardiovascular conditions, even with limited data, improving patient prognosis.

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    Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
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    Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

    Published on: July 20, 2022

    Related Experiment Videos

    Last Updated: Jul 19, 2026

    Behavioral Determination of Stimulus Pair Discrimination of Auditory Acoustic and Electrical Stimuli Using a Classical Conditioning and Heart-rate Approach
    10:50

    Behavioral Determination of Stimulus Pair Discrimination of Auditory Acoustic and Electrical Stimuli Using a Classical Conditioning and Heart-rate Approach

    Published on: June 6, 2012

    Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
    08:10

    Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

    Published on: July 20, 2022

    Area of Science:

    • Cardiology
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Early arrhythmia diagnosis is vital for cardiovascular health.
    • Electrocardiograms (ECGs) are standard diagnostic tools.
    • Diagnosing rare arrhythmias is challenging due to limited data.

    Purpose of the Study:

    • To develop a framework for diagnosing both common and rare arrhythmias using ECGs.
    • To address the challenge of limited data in rare disease classification.
    • To improve the accuracy of Computer-Aided Diagnosis (CAD) for arrhythmias.

    Main Methods:

    • Proposed a Mamba-based Prototypical Contrastive Learning framework (MST-PCAS).
    • Utilized a Mamba-based Spatio-Temporal Feature Fusion Network (MST) for ECG modeling.
    • Implemented Prototypical Contrastive Learning with Augmented Feature Separation (PCAS) for enhanced classification.

    Main Results:

    • Achieved superior rare-class recognition accuracies on PTBXL (79.13%) and Chapman (50.72%) datasets.
    • Demonstrated effectiveness in generalized Few-Shot Learning (FSL) settings.
    • Successfully identified both common and rare arrhythmia classes.

    Conclusions:

    • The MST-PCAS framework effectively diagnoses arrhythmias, including rare types.
    • This approach significantly improves rare-class recognition in ECG analysis.
    • The study offers a promising solution for challenging few-shot learning problems in medical diagnostics.