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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

1.9K
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...
1.9K
Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

864
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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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...
471
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,...
159
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

3.0K
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...
3.0K

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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Electrocardiogram analysis for cardiac arrhythmia classification and prediction through self attention based auto

Ameet Shah1, Dhanpratap Singh2, Heba G Mohamed3

  • 1School of Computer Science and Engineering, Lovely Professional University, Grand Trunk Rd, Phagwara, 144411, Punjab, India.

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|March 18, 2025
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Summary

A new artificial intelligence auto-encoder algorithm effectively classifies cardiac arrhythmia from ECG signals, significantly reducing noise and improving diagnostic accuracy for heart conditions. This automated approach offers a promising solution for early detection and management of heart disease.

Keywords:
Artificial intelligenceAtrial fibrillationCardiac arrhythmiaDeep learning classificationDense neural networkKalman filterPredictionSelf-attention mechanism

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Cardiology

Background:

  • Sudden cardiac arrest is a growing risk, particularly in young individuals.
  • Cardiac arrhythmia increases susceptibility to various heart diseases.
  • Manual classification of ECG signals for arrhythmia is prone to errors, necessitating automated solutions.

Purpose of the Study:

  • To develop and evaluate an automated strategy for accurate cardiac arrhythmia classification using ECG signals.
  • To improve the precision and recall of arrhythmia detection compared to existing methods.
  • To address the need for reliable ECG signal analysis in diagnosing complex cardiac conditions.

Main Methods:

  • A novel self-attention artificial intelligence auto-encoder (AE) algorithm was proposed for ECG classification.
  • A modified Kalman filter was employed for pre-processing ECG signals to reduce noise.
  • The algorithm was trained and tested using the MIT-BIH arrhythmia database.

Main Results:

  • The modified Kalman filter pre-processing improved signal-to-noise ratio (SNRimp) and reduced root-mean-square error (RMSE) and percentage residual deviation (PRD%).
  • The self-attention AE algorithm achieved high classification performance: 99.91% precision, 99.86% recall, and 99.71% accuracy.
  • The proposed system demonstrated superior performance compared to existing models in classifying cardiac arrhythmia.

Conclusions:

  • The self-attention AE algorithm is an effective strategy for cardiac arrhythmia classification.
  • The developed method significantly reduces ECG noise and enhances the visibility of key waveform components.
  • This AI-driven approach shows promise for improving the diagnosis of complex cardiac conditions and addressing real-world heart problems.