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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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Introduction
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Definition
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Related Experiment Video

Updated: May 8, 2026

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

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

Published on: April 11, 2025

A practical reconstructed phase space approach for ECG arrhythmias classification.

Amjed S Al-Fahoum¹, Awni M Qasaimeh

¹Biomedical Systems and Informatics Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk University , Irbid 21163 , Jordan.

Journal of Medical Engineering & Technology

|September 13, 2013

Summary

A new algorithm classifies life-threatening cardiac arrhythmias like atrial fibrillation (AF), ventricular tachycardia (VT), and ventricular fibrillation (VF) using ECG signal analysis. This method achieves high accuracy in identifying these dangerous heart rhythm abnormalities.

Related Experiment Videos

Last Updated: May 8, 2026

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

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

Published on: April 11, 2025

Area of Science:

Cardiology
Biomedical Engineering
Signal Processing

Background:

Atrial and ventricular arrhythmias are critical causes of sudden cardiac death.
These heart rhythm abnormalities can lead to immediate death or cardiac damage.
Accurate classification of life-threatening arrhythmias is crucial for timely intervention.

Purpose of the Study:

To propose a novel algorithm for classifying life-threatening cardiac arrhythmias.
To analyze the non-linear dynamics of ECG signals for arrhythmia detection.
To develop a classification system with high sensitivity and specificity.

Main Methods:

Utilized a signal processing technique to analyze ECG non-linear dynamics in the time domain.
Employed reconstructed phase space (RPS) analysis to classify arrhythmias based on attractor distribution.
Extracted three simple features from distinct RPS regions representative of different arrhythmias.

Main Results:

The algorithm identified distinct regions in RPS for different arrhythmias.
Validation using 45 ECG signals from the MIT database demonstrated high performance.
Achieved sensitivity ranging from 85.7-100% and specificity from 86.7-100%.

Conclusions:

The proposed RPS-based algorithm effectively classifies life-threatening cardiac arrhythmias.
The method offers a promising approach for automated detection of critical heart rhythm disorders.
High accuracy and specificity suggest clinical utility in arrhythmia diagnosis.