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Related Concept Videos

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
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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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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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ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias

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Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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A Machine Learning Approach for the Detection of QRS Complexes in Electrocardiogram (ECG) Using Discrete Wavelet

Ali Rizwan1, P Priyanga2, Emad H Abualsauod3

  • 1Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Computational Intelligence and Neuroscience
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This study introduces an advanced machine learning method for detecting cardiac abnormalities using support vector machine (SVM) classifiers. The novel approach significantly improves accuracy and reduces detection errors in electrocardiogram (ECG) analysis.

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Machine Learning

Background:

  • Cardiac abnormalities and QRS complex detection are crucial for diagnosing heart conditions.
  • Existing methods for ECG analysis have limitations in sensitivity and specificity.
  • Accurate classification of ECG beats is essential for effective cardiac monitoring.

Purpose of the Study:

  • To develop and validate a modified machine learning approach for detecting cardiac abnormalities.
  • To enhance the accuracy and efficiency of QRS complex detection using support vector machine (SVM) classifiers.
  • To compare the proposed method's performance against existing techniques.

Main Methods:

  • Utilized machine learning, specifically support vector machine (SVM) classifiers, for cardiac abnormality detection.
  • Employed Discrete Wavelet Transform (DWT) for feature extraction from ECG signals.
  • Validated the method by accurately categorizing four types of ECG beats: normal, Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), and Paced beats.

Main Results:

  • Achieved a low detection error rate of 0.45% for cardiac irregularities.
  • Demonstrated superior sensitivity and specificity compared to prevailing approaches.
  • Attained high classification accuracy: 96.67% with MLP-BP and 98.39% with SVM classifiers for ECG beat categorization.

Conclusions:

  • The proposed SVM-based method shows significant potential for accurate cardiac abnormality analysis.
  • SVM classifiers are effective in categorizing diverse ECG beat types using DWT features.
  • This advanced technique offers a promising tool for improving cardiac diagnostics and patient monitoring.