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Related Concept Videos

ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

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. When...
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

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 to...
Electrocardiogram01:29

Electrocardiogram

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

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

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...
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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...
Dysrhythmias II: Classification of Tachyarrhythmias01:28

Dysrhythmias II: Classification of Tachyarrhythmias

Tachyarrhythmias are a type of dysrhythmia where the heart rate exceeds 100 beats per minute. Here are some common types of tachyarrhythmias:Sinus TachycardiaSinus tachycardia originates from increased impulses from the sinus node, leading to an elevated heart rate. It is often triggered by stress, fever, or exercise.Patients may experience palpitations, a sensation of a racing heart, dizziness, and chest discomfort.Causes and Risk Factors: Common causes include physical exertion, emotional...

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Related Experiment Video

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BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals
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ECG beat classification based on discrete wavelet transformation and nearest neighbour classifier.

Swati Banerjee1, M Mitra

  • 1Department of Applied Physics, Faculty of Technology, University of Calcutta, 92, APC Road, Kolkata-700009, India. swatibanerjee29@yahoo.com

Journal of Medical Engineering & Technology
|May 25, 2013
PubMed
Summary

This study introduces a statistical method for classifying Anteroseptal Myocardial Infarction (ASMI) using ECG signals. The Mahalanobis distance approach achieved 95.14% accuracy, outperforming Euclidean distance.

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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Published on: May 23, 2021

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Myocardial Infarction (MI) is a critical condition caused by reduced blood supply to the heart muscle.
  • Classifying specific MI types, like Anteroseptal MI (ASMI), is crucial for timely and accurate diagnosis.
  • Electrocardiogram (ECG) signals contain vital information for diagnosing cardiac events.

Purpose of the Study:

  • To propose a statistical classification method for Anteroseptal Myocardial Infarction (ASMI).
  • To evaluate the effectiveness of different distance metrics in an ECG-based classification system.
  • To enhance the accuracy of automatic ASMI detection from ECG data.

Main Methods:

  • Utilized multi-resolution wavelet analysis and thresholding for noise elimination and feature extraction from ECG signals.
  • Implemented a Nearest Neighbour (NN) classification rule using temporal and amplitude features from chest leads (v1-v4).
  • Compared Euclidean and Mahalanobis distances as metrics for the NN classifier.

Main Results:

  • The Mahalanobis distance-based NN rule achieved a classification accuracy of 95.14%.
  • The Euclidean distance-based NN rule yielded a classification accuracy of 81.83%.
  • Mahalanobis distance proved superior to Euclidean distance for ASMI classification in this study.

Conclusions:

  • The proposed statistical approach, particularly with Mahalanobis distance, offers a highly accurate method for automated ASMI diagnosis.
  • Wavelet analysis and NN classification are effective tools for analyzing ECG signals in cardiology.
  • This method provides a valuable tool for improving the diagnostic capabilities for Anteroseptal Myocardial Infarction.