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Dysrhythmias V: Evaluating Dysrhythmias

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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...

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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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Electrophysiological Assessment of Murine Atria with High-Resolution Optical Mapping

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Published on: February 22, 2018

Ventricular arrhythmias assessment.

J Henriques¹, P Carvalho, P Gil

¹CISUC, Departamento de Engenharia Informática, Universidade de Coimbra, Coimbra. jh@dei.uc.pt

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

|November 16, 2007

Summary

This study introduces a two-level framework using neural networks for ventricular arrhythmias (VA) assessment. The system effectively detects various arrhythmias like PVCs, VT, and VF using ECG data.

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Methods for ECG Evaluation of Indicators of Cardiac Risk, and Susceptibility to Aconitine-induced Arrhythmias in Rats Following Status Epilepticus

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Primary Outcome Assessment in a Pig Model of Acute Myocardial Infarction

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Published on: October 14, 2016

Area of Science:

Cardiology
Biomedical Engineering
Artificial Intelligence in Medicine

Background:

Ventricular arrhythmias (VA) pose a significant risk for sudden cardiac death.
Accurate and timely detection of VA is crucial for effective patient management.
Existing methods for VA detection may have limitations in sensitivity and specificity.

Purpose of the Study:

To propose an integrated, two-level framework for comprehensive ventricular arrhythmias assessment.
To develop and evaluate the performance of a novel detection strategy for VA using artificial intelligence.

Main Methods:

A two-level framework integrating four independent neural networks (NN) for specific detection tasks (signal quality, PVC, VT, VF).
Utilized time and frequency domain features from electrocardiogram (ECG) data, selected via correlation analysis.
Employed an ANFIS (Adaptive Neuro-Fuzzy Inference System) structure as a global classifier in the second layer.

Main Results:

The proposed framework demonstrated high sensitivity and specificity in evaluating ventricular arrhythmias.
Performance was validated using established public MIT-BIH databases.
The integrated approach proved effective for comprehensive VA assessment.

Conclusions:

The developed integrated framework offers an effective strategy for ventricular arrhythmias assessment.
The use of neural networks and ANFIS provides a robust method for ECG-based arrhythmia detection.
This approach holds promise for improving the diagnosis and management of VA.