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Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

750
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.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
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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...
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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....
407
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

474
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...
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Related Experiment Video

Updated: May 24, 2025

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
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ECG Beat-By-Beat Classification Using Hybrid Transformer Neural Network Model in Smart Health.

I Hua Tsai, Bashir I Morshed

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    Summary

    This study introduces a hybrid transformer neural network for early heart disease detection using wearable monitors. The novel model achieves 98% accuracy, enabling efficient smart health applications for cardiac risk prevention.

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    Area of Science:

    • Biomedical Engineering
    • Artificial Intelligence in Healthcare
    • Cardiovascular Disease Research

    Background:

    • Wearable cardiac monitors offer real-time data for heart attack detection via smartphone integration.
    • Early diagnosis and prevention of heart disease are critical for patient outcomes.
    • Existing methods may lack the efficiency and accuracy required for widespread smart health applications.

    Purpose of the Study:

    • To develop an efficient smart health application for preventing and early diagnosing heart disease risk.
    • To introduce a novel hybrid transformer neural network model for enhanced cardiac disease prediction.
    • To evaluate the performance of different input feature modules within the hybrid model.

    Main Methods:

    • Development of a hybrid transformer neural network model by integrating Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) with transformer architectures (Transformer ANN and Transformer CNN).
    • Utilized three distinct input feature modules: top 10 time series features, noise-removed MIT BIH ECG data, and a combination of both.
    • Analyzed model accuracy and power consumption for each feature module, converting the best model into a pre-trained version for a Smart-Health application.

    Main Results:

    • The Transformer Convolutional Neural Network (TCNN) model demonstrated the highest performance, achieving 98% accuracy and a 99% F1 score.
    • The hybrid transformer neural network model proved effective in predicting cardiac diseases.
    • The developed algorithm's correctness and functionality were verified within the Smart-Health application.

    Conclusions:

    • The hybrid transformer neural network model, particularly TCNN, offers superior accuracy in detecting cardiac diseases compared to traditional neural network models.
    • The developed model is suitable for integration into smart health applications for improved cardiovascular disease management.
    • This approach facilitates early diagnosis and prevention of heart disease through advanced AI and wearable technology.