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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...
602
ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

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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,...
217
ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias

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Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
21
Electrocardiogram01:29

Electrocardiogram

2.4K
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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ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

976
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....
976
Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

925
Arrhythmias are irregular heart rhythms occurring when the heart's electrical impulses become abnormal. These disturbances can lead to various symptoms, depending on their severity and the underlying cause. Some common factors contributing to arrhythmias include hypoxia, ischemia, electrolyte imbalances, excessive catecholamine exposure, drug toxicity, and muscle overstretching. Arrhythmias can be classified into two main types based on the rate and site of origin of abnormal heart rhythms.
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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Optimization of Arrhythmia-based ECG-lead Selection for Computer-interpreted Heart Rhythm Classification.

Serhii Reznichenko, Shijie Zhou

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    Summary

    Optimizing electrocardiogram (ECG) lead selection for specific heart arrhythmias using deep learning improves classification accuracy. This study identified optimal ECG lead subsets for common arrhythmias, outperforming the full 12-lead set.

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    Area of Science:

    • Cardiology
    • Artificial Intelligence
    • Medical Diagnostics

    Background:

    • The standard 12-lead electrocardiogram (ECG) has redundant information for heart arrhythmia classification.
    • Previous deep learning (DL) models identified a general optimal 4-lead subset for computer-interpreted ECG (CIE).
    • Clinical criteria for arrhythmias are often lead-specific, suggesting a need for arrhythmia-tailored lead subsets.

    Purpose of the Study:

    • To explore the selection of arrhythmia-specific optimal ECG-lead subsets for DL-based CIE.
    • To enhance heart arrhythmia classification performance by using tailored lead combinations.

    Main Methods:

    • A previously developed DL-based CIE model was employed.
    • The model identified optimal ECG-lead subsets for four common arrhythmias: Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Atrial Fibrillation (AF), and Incomplete Atrioventricular Block (I-AVB).
    • A public dataset from PhysioNet Cardiology Challenge 2020 was utilized for training, validation, and testing.

    Main Results:

    • Optimal lead subsets were identified for each arrhythmia: I-AVB (I, II, aVR, aVL, V1, V3, V5), AF (I, II, aVR, V3), LBBB (I, II, aVR, aVF, V1, V3, V4), and RBBB (I, II, III, aVR, V1, V4, V6).
    • The DL-based CIE model using arrhythmia-specific optimal lead subsets significantly outperformed the full 12-lead ECG set for each arrhythmia classification.
    • Performance improvements were validated on both the internal validation set and an external test dataset.

    Conclusions:

    • Using an arrhythmia-based optimal ECG-lead subset improves classification performance for DL-based CIE compared to the full 12-lead ECG set.
    • This approach achieves comparable or improved accuracy without loss of diagnostic information for specific arrhythmias.
    • Tailoring ECG lead selection to specific arrhythmias enhances the efficiency and accuracy of automated cardiac diagnostic systems.