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Related Concept Videos

Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

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Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin...
474
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

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An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage....
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Electrocardiogram01:29

Electrocardiogram

2.0K
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...
2.0K
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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ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

164
Arrhythmias are disturbances in the heart's rhythm that lead to abnormal heartbeats. These irregularities can originate from different parts of the heart and are classified based on their origin and nature.
Types of Arrhythmias
Sinus Node Arrhythmias
Sinus Bradycardia: Originating from the sinoatrial (SA) node, sinus bradycardia involves slower impulses, resulting in a heart rate of less than 60 beats per minute (bpm). Causes include sleep, vagal stimulation, beta-blockers, hypothyroidism,...
164
Cardiac Action Potential01:30

Cardiac Action Potential

777
Cardiac action potentials are essential for proper heart function, enabling the rhythmic contractions needed for adequate blood circulation. Nodal cells and Purkinje fibers, specialized for electrical conduction, generate these action potentials.
The cardiac action potential process involves a series of phases characterized by the movement of ions across the cardiac cell membranes, leading to the depolarization and repolarization of the cardiac myocytes.
Ionic Basis of Cardiac Action Potentials
777

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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Semantic Segmentation of QRS complex in 12-Lead ECG Signals.

Mateus de Paula Da Silva, Marly G F Costa, Diego Giovani A Vieira

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    Summary

    This study introduces a novel method for precise QRS complex segmentation in 12-lead ECG signals using autoencoders. The approach achieves state-of-the-art F1-scores exceeding 99.90%, significantly improving cardiac rhythm analysis.

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    Area of Science:

    • Biomedical Engineering
    • Cardiovascular Signal Processing
    • Machine Learning in Healthcare

    Background:

    • Accurate segmentation of the QRS complex in electrocardiogram (ECG) signals is crucial for diagnosing cardiac conditions.
    • Existing methods for QRS complex detection often face challenges with noise and signal variability in 12-lead ECGs.

    Purpose of the Study:

    • To develop and evaluate a novel semantic segmentation method for the QRS complex in 12-lead ECG signals.
    • To achieve state-of-the-art performance in QRS complex detection using deep learning techniques.

    Main Methods:

    • The proposed method involves three stages: data preprocessing to generate ground truth vectors, QRS complex segmentation using three distinct autoencoder architectures, and post-processing filters for noise reduction.
    • The St Petersburg INCART 12-lead ECG dataset was utilized for training and validation.

    Main Results:

    • The autoencoder-based semantic segmentation method achieved an F1-score exceeding 99.90% for QRS complex detection.
    • This performance surpasses previously published methods on the same dataset, establishing a new state-of-the-art.

    Conclusions:

    • The developed method demonstrates high accuracy and robustness for QRS complex segmentation in 12-lead ECG signals.
    • This approach holds significant potential for improving automated ECG analysis and clinical decision-making.