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Related Concept Videos

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,...
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Electrocardiogram01:29

Electrocardiogram

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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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Correlation between ECG and Cardiac Cycle01:25

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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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Electrophysiology of Normal Cardiac Rhythm01:19

Electrophysiology of Normal Cardiac Rhythm

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The normal cardiac rhythm is a synchronized electrical activity that facilitates the regular and coordinated contraction of the heart muscle. This process is essential for efficient blood circulation throughout the body. The fundamental elements involved in establishing and maintaining this rhythm include the unique electrical properties of cardiac muscle cells, the sinoatrial (SA) node's pacemaker function, the specialized conducting system, and the ionic mechanisms underlying each phase...
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Disturbances in Heart Rhythm01:28

Disturbances in Heart Rhythm

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Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow...
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Person identification with arrhythmic ECG signals using deep convolution neural network.

Awabed Al-Jibreen1, Saad Al-Ahmadi2, Saiful Islam3

  • 1Computer Science Department, College of Computer and Information Sciences, King Saud University, 11543, Riyadh, Saudi Arabia. 439204595@student.ksu.edu.sa.

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This study explores how heart rhythm irregularities (arrhythmias) impact electrocardiogram (ECG) biometrics. A new lightweight deep learning model achieves high accuracy in identifying individuals even with arrhythmias.

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Area of Science:

  • Biometrics
  • Cardiology
  • Machine Learning

Background:

  • Electrocardiogram (ECG) biometrics are increasingly used for identification.
  • Most ECG biometric systems overlook individual health states and arrhythmias.
  • Health-state annotated ECG data is crucial for robust identification.

Purpose of the Study:

  • To investigate the impact of arrhythmias on ECG-based person identification.
  • To propose a novel, efficient deep learning model for arrhythmia-aware ECG biometrics.
  • To evaluate the model's performance across various arrhythmia types.

Main Methods:

  • Developed a lightweight Convolutional Neural Network (CNN) using depth-wise separable convolution (DWSC).
  • Utilized the MIT-BIH dataset, annotated for health states and nine arrhythmia types.
  • Tested the system's ability to differentiate individuals with overlapping normal and arrhythmic heartbeats.

Main Results:

  • Achieved 99.28% accuracy for normal heartbeats and 93.81% for arrhythmic heartbeats.
  • Demonstrated differential impact of various arrhythmia types on biometric recognition.
  • Outperformed existing models in mean accuracy for ECG-based person identification.

Conclusions:

  • ECG-based biometric systems are significantly affected by arrhythmias.
  • The proposed DWSC-based CNN model offers high accuracy and efficiency for arrhythmia-aware identification.
  • Considering health-state annotations is vital for developing reliable ECG biometric systems.