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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

2.6K
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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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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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...
671
Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

44
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...
44
Disturbances in Heart Rhythm01:29

Disturbances in Heart Rhythm

1.1K
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 heart...
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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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A Data-Driven Preprocessing Framework for Atrial Fibrillation Intracardiac Electrocardiogram Analysis.

Xiangzhen Kong1, Vasanth Ravikumar1, Siva K Mulpuru2

  • 1Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA.

Entropy (Basel, Switzerland)
|February 25, 2023
PubMed
Summary
This summary is machine-generated.

This study optimized bandpass filter settings for analyzing cardiac electrograms in Atrial Fibrillation (AF). A data-driven approach identified 15 Hz as the optimal upper bound for improved dominant frequency and multiscale frequency analysis.

Keywords:
DBSCANPearson’s correlationatrial fibrillationbandpass filtercatheter ablationearth mover’s distanceintracardiac electrogramsmultiscale frequency

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High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation
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Area of Science:

  • Biomedical Engineering
  • Computational Cardiology
  • Signal Processing

Background:

  • Atrial Fibrillation (AF) is a common arrhythmia requiring catheter ablation.
  • Intracardiac electrograms (iEGMs) are analyzed using signal processing for ablation targeting.
  • Dominant Frequency (DF) and Multiscale Frequency (MSF) are key metrics, but filter choice impacts analysis.

Purpose of the Study:

  • To develop and validate a data-driven preprocessing framework for iEGM analysis.
  • To optimize the bandpass filter upper bound (BP¯th) for improved DF and MSF analysis.
  • To assess the impact of BP¯th on iEGM analysis in AF patients.

Main Methods:

  • Utilized DBSCAN clustering for data-driven optimization of BP¯th.
  • Applied the optimized framework to clinically recorded iEGMs from AF patients.
  • Evaluated filter performance using the Dunn index and compared DF/MSF results.

Main Results:

  • The data-driven framework with BP¯th = 15 Hz demonstrated optimal performance based on the Dunn index.
  • Different BP¯th values significantly affected subsequent DF and MSF analyses.
  • Removal of noisy and contact-loss leads is crucial for accurate iEGM analysis.

Conclusions:

  • A data-driven approach provides optimal bandpass filter settings for iEGM analysis.
  • BP¯th = 15 Hz is recommended for robust DF and MSF analysis in AF.
  • Preprocessing, including noise and lead artifact removal, is essential for reliable iEGM interpretation.