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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...

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...

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Comparing different wavelet transforms on removing electrocardiogram baseline wanders and special trends.

Chao-Chen Chen^1,2, Fuchiang Rich Tsui^3,4

¹Tsui Laboratory, Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.

BMC Medical Informatics and Decision Making

|December 31, 2020

Summary

Wavelet transforms effectively remove electrocardiogram (ECG) baseline wander artifacts. Daubechies-3 and Symlets-3 wavelets showed the best performance in this study, improving ECG signal analysis.

Keywords:

Baseline wander Electrocardiogram Mean-square-error Wavelet transform

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Area of Science:

Biomedical Signal Processing
Cardiovascular Diagnostics

Background:

Electrocardiogram (ECG) signals are crucial for diagnosing heart conditions.
Low-frequency baseline wander (BW) artifacts corrupt ECG signals, hindering accurate analysis.
Wavelet transforms (WTs) offer superior time-frequency analysis compared to traditional filters for signal artifact removal.

Purpose of the Study:

To evaluate the efficacy of different wavelet families for removing baseline wander (BW) from ECG signals.
To compare the performance of 14 wavelets across 5 families in eliminating BW artifacts.

Main Methods:

A semi-synthetic ECG dataset was created using data from the Physionet QT Database.
Artificial baseline wanders (sinusoidal, spikes, step functions) were superimposed onto 105 ECG excerpts.
Fourteen commonly used wavelets were implemented and evaluated up to 12 WT levels, using Mean Square Error (MSE) as the evaluation metric.

Main Results:

Daubechies-3 and Symlets-3 wavelets demonstrated the best performance, achieving an MSE of 0.0044 at 7 WT levels.
Wavelet transforms generally removed various BW types effectively, with slightly lower performance for spike and step functions.
WTs accurately identified the temporal location of impulse edges in the ECG signals.

Conclusions:

Wavelet transforms are effective for removing diverse ECG baseline wander artifacts.
Daubechies-3 and Symlets-3 wavelets are optimal choices for BW removal.
This research supports the development of real-time ECG processing for clinical decision support systems.