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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....
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Electrocardiogram01:29

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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

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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
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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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Pulse rhythm01:30

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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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Dysrhythmias III: Characteristics of Dysrhythmias01:29

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Dysrhythmias, also known as arrhythmias, are irregular heart rhythms that result from abnormal electrical activity in the heart, affecting its ability to circulate blood efficiently. Tachyarrhythmias, a subset of dysrhythmias, are characterized by abnormally fast heart rates exceeding 100 beats per minute. Here are some types of tachyarrhythmias with their distinct ECG features:Sinus Tachycardia:Sinus tachycardia presents a regular heart rhythm with an increased rate of 101-180 beats per...
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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Advanced Time-Frequency Methods for ECG Waves Recognition.

Ala'a Zyout1, Hiam Alquran1, Wan Azani Mustafa2,3

  • 1Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid 21163, Jordan.

Diagnostics (Basel, Switzerland)
|January 21, 2023
PubMed
Summary
This summary is machine-generated.

This study shows that analyzing a single electrocardiogram (ECG) wave using time-frequency analysis and deep learning can accurately detect heart rhythms like normal, tachycardia, and bradycardia.

Keywords:
CNNECGResNet101ShuffleNetheart rhythmiris-spectrogramscalogram

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Electrocardiogram (ECG) wave recognition is crucial for diagnosing heart conditions.
  • Traditional methods struggle to differentiate normal, tachycardia, and bradycardia rhythms using only time or frequency domains.

Purpose of the Study:

  • To evaluate the effectiveness of iris-spectrogram and scalogram time-frequency representations for ECG beat wave analysis.
  • To assess the performance of ResNet101 and ShuffleNet deep convolutional neural networks (CNNs) for rhythm classification.

Main Methods:

  • Utilized iris-spectrogram and scalogram for spectral representation of individual ECG beat waves (P, QRS, T).
  • Employed ResNet101 and ShuffleNet CNN architectures for feature extraction and classification.
  • Compared classification accuracy across different wave segments and spectrum representations.

Main Results:

  • Achieved a 98.3% accuracy using ResNet101 with T-wave scalogram for rhythm detection.
  • Obtained 94.4% accuracy with ResNet101 and QRS-wave iris-spectrogram.
  • Demonstrated the efficacy of time-frequency analysis on single ECG waves for rhythm classification.

Conclusions:

  • Deep features derived from time-frequency representations of single ECG waves enable accurate detection of basic heart rhythms.
  • Scalogram of the T-wave combined with ResNet101 shows superior performance for distinguishing normal, tachycardia, and bradycardia rhythms.