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Related Concept Videos

Sleep Apnea01:21

Sleep Apnea

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Sleep apnea is a condition where breathing stops intermittently during sleep, often leading to significant health issues. Each episode can last from 10 to 20 seconds or more and is frequently accompanied by a brief arousal from sleep. This disturbance, largely unnoticed by the individual, can lead to severe daytime fatigue. Commonly, individuals seek help after being informed by their partners about loud snoring and noticeable breathing pauses during sleep.
The condition is more prevalent among...
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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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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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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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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.
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ECG Signal Modeling Using Volatility Properties: Its Application in Sleep Apnea Syndrome.

Maryam Faal1, Farshad Almasganj1

  • 1Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.

Journal of Healthcare Engineering
|July 26, 2021
PubMed
Summary

This study introduces a novel ARIMA-EGARCH model to analyze electrocardiogram (ECG) signals in sleep apnea syndrome, effectively capturing both linear and nonlinear signal characteristics for improved accuracy.

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Single-lead ECG signals exhibit time-varying mean and variance, posing challenges for accurate modeling in sleep apnea syndrome.
  • Understanding the stochastic nature and volatility of ECG signals is crucial for diagnosing sleep apnea.

Purpose of the Study:

  • To develop and evaluate a mathematical model for estimating the mean and variance of single-lead ECG signals in sleep apnea syndrome.
  • To leverage the volatility property of ECG signals for enhanced diagnostic modeling.

Main Methods:

  • Proposed a novel ARIMA-EGARCH model to decompose ECG nonstationarity into homoscedastic (ARIMA) and heteroscedastic (EGARCH) components.
  • Segmented ECG signals into one-minute intervals and applied statistical tests to examine heteroskedasticity.
  • Estimated model orders using the Bayesian Information Criterion (BIC) and assessed performance using MSE, RMSE, MAE, and MAPE.

Main Results:

  • The proposed ARIMA-EGARCH model demonstrated superior performance compared to other models for analyzing both apneic and normal ECG signals.
  • The model effectively captured both linear and nonlinear characteristics inherent in the ECG signals of sleep apnea patients.

Conclusions:

  • The ARIMA-EGARCH model provides a robust framework for modeling ECG signal volatility in sleep apnea syndrome.
  • This approach offers improved accuracy in estimating ECG signal parameters, potentially aiding in sleep apnea diagnosis and management.