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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

4.8K
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 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....
9.7K
Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

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Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...
216
Acute Coronary Syndrome III: Diagnostic Studies01:30

Acute Coronary Syndrome III: Diagnostic Studies

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Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...
112
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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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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Updated: Dec 1, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Computational Diagnostic Techniques for Electrocardiogram Signal Analysis.

Liping Xie1, Zilong Li1, Yihan Zhou1

  • 1College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China.

Sensors (Basel, Switzerland)
|November 10, 2020
PubMed
Summary
This summary is machine-generated.

Computer-aided techniques using electrocardiogram (ECG) signals offer fast, accurate detection of cardiovascular diseases (CVDs). These advanced methods, especially End-to-End models, simplify analysis and improve diagnostic accuracy for better heart health monitoring.

Keywords:
classificationdeep learningelectrocardiogramfeature engineeringmachine learning

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Area of Science:

  • Biomedical Engineering
  • Cardiology
  • Artificial Intelligence in Healthcare

Background:

  • Cardiovascular diseases (CVDs) are the leading global cause of mortality.
  • Early detection of CVDs is crucial for preventing cardiovascular death.
  • Electrocardiogram (ECG) signals provide vital information about heart activity.

Purpose of the Study:

  • To summarize the latest computational diagnostic techniques for estimating CVD conditions using ECG signals.
  • To discuss the complete ECG signal analysis procedure, including preprocessing, feature engineering, and classification.
  • To highlight the advantages of End-to-End models in ECG-based CVD diagnosis.

Main Methods:

  • Review of computational diagnostic techniques for ECG signal analysis.
  • Discussion of data preprocessing, feature engineering, and classification steps.
  • Focus on End-to-End models integrating feature extraction and classification.

Main Results:

  • Computer-aided techniques demonstrate success in fast and accurate CVD identification from ECG signals.
  • End-to-End models simplify analysis, showing excellent accuracy and robustness.
  • Portable devices offer new avenues for continuous cardiovascular monitoring.

Conclusions:

  • Computational diagnostic techniques for ECG analysis hold significant potential for healthcare professionals.
  • Widespread application of these techniques can benefit both patients and sub-healthy individuals in daily life.
  • Advancements in AI-driven ECG analysis pave the way for improved cardiovascular health outcomes.