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

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...

Dysrhythmias V: Evaluating Dysrhythmias

Dysrhythmias V: Evaluating Dysrhythmias

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...

Dysrhythmias II: Classification of Tachyarrhythmias

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...

Disturbances in Heart Rhythm

Disturbances in Heart Rhythm

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...

Dysrhythmias III: Characteristics of Dysrhythmias

Dysrhythmias III: Characteristics of Dysrhythmias

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 minute.

Dysrhythmias IV: Characteristics of Bradyarrhythmias

Dysrhythmias IV: Characteristics of Bradyarrhythmias

Bradyarrhythmias are cardiac rhythm disorders characterized by a slower-than-normal heart rate, typically defined as fewer than 60 beats per minute. Some of which are discussed here:Sinus BradycardiaSinus bradycardia presents a heart rate lower than 60 beats per minute, with a regular rhythm originating from the SA node. The ECG typically shows normal P waves preceding each QRS complex, a normal PR interval (0.12 to 0.20 seconds), and a normal QRS duration (0.06 to 0.10 seconds).First-Degree AV...

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Related Experiment Video

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Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

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Wavelet-based features for characterizing ventricular arrhythmias in optimizing treatment options.

K Balasundaram¹, S Masse, K Nair

¹Ryerson University.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 19, 2012

Summary

This study introduces a method to classify ventricular arrhythmias, distinguishing between ventricular tachycardia (VT) and ventricular fibrillation (VF). Wavelet analysis of surface electrograms achieved 75% accuracy in categorizing these heart rhythm disorders.

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Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Published on: July 20, 2022

Area of Science:

Cardiology
Biomedical Engineering
Signal Processing

Background:

Ventricular arrhythmias, including ventricular tachycardia (VT) and ventricular fibrillation (VF), stem from abnormal heart electrical activity.
Differentiating between VT and VF is crucial for treatment decisions, as VF is lethal and often managed with implantable defibrillators, while VT may have other therapeutic options.
The indistinct boundary between VT and VF can lead to misclassification, potentially resulting in unnecessary shocks from defibrillators for patients with overlapping conditions.

Purpose of the Study:

To develop a quantifiable method for classifying ventricular arrhythmias into VT, VF, and an overlap zone (VT-VF candidates).
To enable objective analysis of arrhythmias in the overlap zone, determining their propensity towards VT or VF.
To improve treatment strategies by providing a more precise diagnosis of ventricular arrhythmias.

Main Methods:

Utilized wavelet analysis to extract features from surface electrograms of ventricular arrhythmias.
Analyzed a database of 24 human ventricular arrhythmia tracings from the MIT-BIH arrhythmia database.
Extracted wavelet-based features that effectively discriminate between VT, VF, and VT-VF groups.

Main Results:

Successfully extracted discriminating wavelet-based features from surface electrograms.
Achieved an overall accuracy of 75% in classifying ventricular arrhythmias into three distinct groups: VT, VF, and VT-VF candidates.
Demonstrated the potential for an objective approach to analyzing and scaling ventricular arrhythmias.

Conclusions:

Wavelet analysis of surface electrograms offers a promising approach for classifying ventricular arrhythmias.
The proposed method can help differentiate between VT, VF, and the challenging overlap zone.
This technique may lead to more personalized and effective treatment decisions for patients with ventricular arrhythmias.