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Introduction Cardiac Emergencies

Introduction Cardiac Emergencies

Cardiac emergencies are critical situations involving the heart that require immediate medical intervention to prevent severe complications or death. These emergencies often arise from underlying heart conditions that impair the heart's ability to function correctly.Types of Cardiac EmergenciesThe most common types of cardiac emergencies include Acute Coronary Syndrome (ACS), myocardial infarction (MI), cardiac arrest, and heart failure.Acute Coronary Syndrome (ACS)Acute Coronary Syndrome (ACS)...

Pulse rhythm

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Electrocardiogram

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Related Experiment Video

Updated: Jun 9, 2026

A Modified Sonographic Algorithm for Image Acquisition in Life-Threatening Emergencies in the Critically Ill Newborn

A Modified Sonographic Algorithm for Image Acquisition in Life-Threatening Emergencies in the Critically Ill Newborn

Published on: April 7, 2023

Sequential algorithm for life threatening cardiac pathologies detection based on mean signal strength and EMD

Emran M Abu Anas¹, Soo Y Lee, Md K Hasan

¹Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh.

Biomedical Engineering Online

|September 7, 2010

Summary

A new algorithm improves automated external defibrillator (AED) accuracy in detecting life-threatening ventricular arrhythmias like ventricular tachycardia (VT) and ventricular fibrillation (VF). This EMD-based method enhances safety and efficacy for public use.

Related Experiment Videos

Last Updated: Jun 9, 2026

A Modified Sonographic Algorithm for Image Acquisition in Life-Threatening Emergencies in the Critically Ill Newborn

A Modified Sonographic Algorithm for Image Acquisition in Life-Threatening Emergencies in the Critically Ill Newborn

Published on: April 7, 2023

Area of Science:

Cardiology
Biomedical Engineering
Signal Processing

Background:

Ventricular tachycardia (VT) and ventricular fibrillation (VF) are critical cardiac arrhythmias requiring rapid detection.
Automated external defibrillators (AEDs) use algorithms to identify shockable rhythms and deliver electrical therapy.
Enhancing AED safety and efficacy through improved rhythm discrimination is crucial for public health.

Purpose of the Study:

To develop and validate a novel sequential detection algorithm for distinguishing VT and VF from other arrhythmias.
To improve the accuracy of AEDs in identifying shockable cardiac rhythms.
To differentiate between VT and VF, and further classify VF subtypes.

Main Methods:

A sequential detection algorithm utilizing mean signal strength, Empirical Mode Decomposition (EMD) intrinsic mode functions (IMFs), and heart rate determination.
Waveform quantification via mean absolute signal value to initially separate VT/VF from other arrhythmias.
EMD IMFs and rate/amplitude analysis are used sequentially to differentiate VF from VT and VF subtypes.

Main Results:

The proposed algorithm demonstrated superior performance compared to existing methods (HILB, PSR, SPEC, TCI, Count).
Evaluation used multiple standard databases: MIT-BIH Arrhythmia, Creighton University Ventricular Tachyarrhythmia, and MIT-BIH Malignant Ventricular Arrhythmia.
Key performance metrics including sensitivity, specificity, positive predictivity, and accuracy were analyzed for VTVF, VF, and VT (≥180 bpm).

Conclusions:

The novel EMD-based algorithm significantly outperforms existing techniques in detecting life-threatening cardiac arrhythmias.
The proposed method offers improved accuracy and reliability for AED applications.
This advancement contributes to safer and more effective automated external defibrillation in critical care settings.