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Electrocardiogram Fundamentals

Electrocardiogram Fundamentals

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 to...

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

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Arrhythmias are disturbances in the heart's rhythm that lead to abnormal heartbeats. These irregularities can originate from different parts of the heart and are classified based on their origin and nature.
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Sinus Node Arrhythmias
Sinus Bradycardia: Originating from the sinoatrial (SA) node, sinus bradycardia involves slower impulses, resulting in a heart rate of less than 60 beats per minute (bpm). Causes include sleep, vagal stimulation, beta-blockers, hypothyroidism, and...

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Updated: May 14, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

Multi-lead QRS detection using window pairs.

Sami Torbey¹, Selim G Akl, Damian P Redfearn

¹Queen’s University, Kingston, Ontario, Canada.

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

|February 1, 2013

Summary

This summary is machine-generated.

This study introduces a novel multi-lead QRS detection algorithm. It achieves high accuracy and efficiency by adapting to signal variations and noise, minimizing detection errors.

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Electrophysiological Analysis of human Pluripotent Stem Cell-derived Cardiomyocytes (hPSC-CMs) Using Multi-electrode Arrays (MEAs)

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Published on: May 12, 2017

Area of Science:

Biomedical Engineering
Cardiovascular Signal Processing

Background:

Accurate QRS complex detection is crucial for electrocardiogram (ECG) analysis.
Existing algorithms face challenges with varying QRS morphology and noise levels.

Purpose of the Study:

To develop a robust and efficient multi-lead QRS detection algorithm.
To improve QRS detection accuracy across diverse signal conditions.

Main Methods:

A novel algorithm utilizing a single equation with dual window widths.
Generation of a feature signal and adaptive detection threshold.
Automatic confidence value assignment for multi-lead analysis.

Main Results:

Achieved a low detection error rate of 0.29% on the MIT-BIH Arrhythmia Database.
Demonstrated adaptability to changes in QRS morphology and noise.
Algorithm is computationally efficient.

Conclusions:

The proposed algorithm offers a significant advancement in multi-lead QRS detection.
Its robustness and efficiency make it suitable for clinical applications.
Confidence value assignment enhances the reliability of multi-lead ECG interpretation.