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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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Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

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Arrhythmias are irregular heart rhythms occurring when the heart's electrical impulses become abnormal. These disturbances can lead to various symptoms, depending on their severity and the underlying cause. Some common factors contributing to arrhythmias include hypoxia, ischemia, electrolyte imbalances, excessive catecholamine exposure, drug toxicity, and muscle overstretching. Arrhythmias can be classified into two main types based on the rate and site of origin of abnormal heart rhythms.
885
Disturbances in Heart Rhythm01:28

Disturbances in Heart Rhythm

894
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...
894
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

459
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....
459
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

3.3K
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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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Automated Classification of Cardiac Arrhythmia using Short-Duration ECG Signals and Machine Learning.

Amar Bahadur Biswakarma1, Jagdeep Rahul2, Kurmendra Kurmendra3

  • 1Electronics and Communication Engineering, Rajiv Gandhi University, Doimukh, Itanagar, Itanagar, Arunachal Pradesh, 791112, INDIA.

Biomedical Physics & Engineering Express
|January 13, 2025
PubMed
Summary
This summary is machine-generated.

This study improved cardiac arrhythmia detection using signal processing and machine learning. The Support Vector Machine classifier achieved high accuracy in identifying five types of heart rhythm abnormalities.

Keywords:
Cardiac arrhythmiaECGL-BBBMachine learningPACPVCR-BBB

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Area of Science:

  • Biomedical Engineering
  • Cardiology
  • Signal Processing

Background:

  • Accurate cardiac arrhythmia detection is vital for preventing mortality.
  • Existing methods for electrocardiogram (ECG) signal analysis face challenges with noise and classification accuracy.

Purpose of the Study:

  • To develop and evaluate an automated system for classifying five types of cardiac arrhythmias.
  • To enhance the accuracy of arrhythmia detection through advanced signal processing and machine learning techniques.

Main Methods:

  • ECG signals were preprocessed using a dual-stage Discrete Wavelet Transform (DWT) and median filter to remove noise.
  • QRS regions were segmented, and nine temporal features were extracted for analysis.
  • Five cardiac arrhythmias (normal, PVC, PAC, R-BBB, L-BBB) were classified using six different machine learning algorithms.

Main Results:

  • The Support Vector Machine (SVM) and Ensemble Tree classifiers showed superior performance.
  • The SVM classifier, using a Gaussian kernel, achieved high accuracy (98.97%), sensitivity (97.44%), specificity (99.36%), and positive predictive value (97.44%).

Conclusions:

  • The integrated approach of DWT-based noise reduction, feature extraction, and SVM classification offers a robust method for automatic cardiac arrhythmia detection.
  • This methodology shows significant potential for improving the diagnosis of cardiac arrhythmias in clinical settings and trials.