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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

2.3K
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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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...
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Instrumentation Amplifier01:25

Instrumentation Amplifier

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An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...
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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....
932
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

5.4K
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...
5.4K
Pulse rhythm01:30

Pulse rhythm

803
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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Pre-Processing techniques and artificial intelligence algorithms for electrocardiogram (ECG) signals analysis: A

Muhammad Farhan Safdar1, Robert Marek Nowak1, Piotr Pałka2

  • 1Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland.

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Summary

Artificial intelligence (AI) significantly enhances electrocardiogram (ECG) analysis, with deep learning and transformer models achieving up to 98% accuracy. Wearable devices offer convenient, accurate remote monitoring for AI calibration.

Keywords:
Agent based modelingData augmentationDeep learningElectrocardiogramsSpectrogramsWearable devices

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Area of Science:

  • Cardiology and Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Signal Processing

Background:

  • Electrocardiogram (ECG) signals are crucial for assessing cardiac electrical activity.
  • AI methods, including machine learning (ML) and deep learning (DL), have advanced ECG analysis over the past decade.
  • Traditional signal processing methods are evolving with AI-driven approaches.

Purpose of the Study:

  • To review the application of AI in ECG signal analysis from 2012-2022.
  • To categorize ECG analysis methods, data sources, and emerging trends.
  • To evaluate the performance and accuracy improvements achieved by different AI models.

Main Methods:

  • Review of AI techniques applied to ECG analysis, categorizing them into classical signal processing, ML, and DL (recursive models, transformers, hybrid models).
  • Analysis of data sources, including hospital-based machines and wearable devices, and benchmark datasets (e.g., Physio-Net, MIT-BIH, PTB).
  • Inclusion of new trends such as advanced pre-processing, data augmentation, simulations, and agent-based modeling.

Main Results:

  • Significant improvements in ECG analysis accuracy attributed to ML, DL, hybrid, and transformer models, with transformers reaching 98% accuracy.
  • Convolutional neural networks and hybrid models demonstrated high efficiency.
  • Wearable devices show promise for continuous monitoring and AI model calibration, achieving 82%-83% accuracy.
  • Spectrogram generation via Fourier and wavelet transformations yields 90%-95% accuracy; geometrical data augmentation is effective, but extraction/concatenation methods require further development.
  • Agent-based modeling and simulation are reviewed for cardiovascular risk assessment.

Conclusions:

  • AI, particularly DL and transformer models, has substantially improved the accuracy and sophistication of ECG analysis.
  • Wearable technology integrated with AI presents a viable, accessible option for remote cardiac monitoring and AI model refinement.
  • Advanced signal processing and data augmentation techniques are key to enhancing ECG analysis, with simulation methods offering potential for risk prediction.