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ECG Interpretation of Rhythms

ECG Interpretation of Rhythms

An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage....

Electrocardiogram

Electrocardiogram

An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...

Correlation between ECG and Cardiac Cycle

Correlation between ECG and Cardiac Cycle

The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...

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...

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FDP-TF: A fast two-pass trend filtering for ECG delineation.

Tom Trigano¹, Yann Sepulcre², Matan Masika¹

¹Shamoon College of Engineering, Department of Electrical Engineering, Jabotinsky St, 84, Ashdod Campus, Ashdod, Israel.

Computers in Biology and Medicine

|September 1, 2025

Summary

A new Fast Double-Pass Trend Filtering (FDP-TF) algorithm significantly speeds up electrocardiogram (ECG) delineation. This novel method offers orders of magnitude faster processing without sacrificing accuracy for cardiac monitoring.

Keywords:

Adaptive trend filtering Compressive sensing Delineation ECG signal processing QT dataset

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Published on: December 22, 2023

Area of Science:

Biomedical Engineering
Signal Processing
Cardiology

Background:

Electrocardiogram (ECG) delineation is crucial for cardiac monitoring and diagnosis, involving the identification of key waveform features like P, QRS, and T waves.
The Adaptive Trend Filtering (ATF) algorithm shows promise for ECG denoising and delineation by detecting local extrema.
Previous ATF implementations using the Alternating Direction Method of Multipliers (ADMM) suffered from long execution times, hindering practical use.

Purpose of the Study:

To present Fast Double-Pass Trend Filtering (FDP-TF), a significantly faster reformulation of the ATF algorithm.
To evaluate the performance and speed of the novel FDP-TF algorithm for ECG signal processing.

Main Methods:

Developed Fast Double-Pass Trend Filtering (FDP-TF) as a complete reformulation of the Adaptive Trend Filtering (ATF) algorithm.
Evaluated FDP-TF's performance and execution time on ECG datasets, including the QT dataset.

Main Results:

The FDP-TF algorithm achieved a speedup of several orders of magnitude compared to previous ATF implementations.
FDP-TF demonstrated no loss in algorithm performance, maintaining accuracy comparable to state-of-the-art methods.
The enhanced speed makes FDP-TF suitable for processing long ECG recordings, real-time analysis, and wearable devices.

Conclusions:

FDP-TF offers a compelling solution for fast and accurate ECG delineation.
The algorithm's efficiency and performance make it ideal for various clinical and portable applications.
This advancement facilitates more practical and widespread use of advanced ECG analysis techniques.