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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

7.8K
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...
7.8K
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....
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Dysrhythmias III: Characteristics of Dysrhythmias01:29

Dysrhythmias III: Characteristics of Dysrhythmias

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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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Pulse rhythm01:30

Pulse rhythm

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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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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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Patient-specific ECG beat classification technique.

Manab K Das1, Samit Ari1

  • 1Department of Electronics and Communication Engineering , National Institute of Technology , Rourkela , India.

Healthcare Technology Letters
|November 27, 2015
PubMed
Summary

This study introduces an automated system for electrocardiogram (ECG) beat classification, improving diagnosis of critical heart conditions. The method accurately identifies various heart rhythms, demonstrating strong performance on standard databases.

Keywords:
BFO algorithmECGINCART databaseLMS-based multiclass SVM classifierLagrange multiplierMIT-BIH arrhythmia databaseS-transformSt. Petersburg Institute of Cardiological Technics databaseStockwell transformautomated diagnosisautomated diagnostic systembacteria foraging optimisationclassification error minimizationcombined feature vectorcritical heart conditionelectrocardiographyfeature extractionleast mean square-based multiclass support vector machineleast squares approximationsmedical signal processingmorphological feature extractionoptimised feature vectorpatient-specific electrocardiogram beat classification techniquesignal classificationsupport vector machinessupra ventricular ectopic beattiming featurestransformsventricular ectopic beatweight vector

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Cardiology

Background:

  • Electrocardiogram (ECG) beat classification is crucial for diagnosing critical heart conditions.
  • Existing methods require improvement for accurate and timely diagnosis.

Purpose of the Study:

  • To propose an automated diagnostic system for classifying five types of ECG heartbeats.
  • To enhance the accuracy and generalizability of ECG beat classification.

Main Methods:

  • Integration of Stockwell transform (ST) for feature extraction.
  • Optimization of features using bacteria foraging optimization (BFO) algorithm.
  • Classification using a least mean square (LMS)-based multiclass support vector machine (SVM).

Main Results:

  • Achieved high average accuracy (98.6% for V, 98.2% for S) and sensitivity (91.7% for V, 74.7% for S) on the MIT-BIH database.
  • Demonstrated superior performance and generalizability compared to other reported heartbeat classification techniques on the INCART database.

Conclusions:

  • The proposed integrated approach offers a robust method for automated ECG beat classification.
  • The system shows significant potential for improving the early diagnosis of cardiac arrhythmias.