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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

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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.
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An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
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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.
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Related Experiment Videos

An ECG signals compression method and its validation using NNs.

Catalina Monica Fira¹, Liviu Goras

¹Institute of Computer Science, Iasi, Romania. mfira@scs.etc.tuiasi.ro

IEEE Transactions on Bio-Medical Engineering

|April 9, 2008

Summary

This summary is machine-generated.

A novel electrocardiogram (ECG) compression algorithm uses signal processing and Lempel-Ziv-Welch (LZW) coding. It achieves efficient data reduction while maintaining signal fidelity, validated by neural network analysis.

Related Experiment Videos

Area of Science:

Biomedical Engineering
Signal Processing
Artificial Intelligence

Background:

Electrocardiogram (ECG) data requires efficient compression for storage and transmission.
Existing compression methods may not optimally balance compression ratio and signal fidelity.
Accurate cardiac beat classification is crucial for diagnosis and analysis.

Purpose of the Study:

To introduce a new ECG signal compression algorithm.
To evaluate the algorithm's performance using diverse cardiac beat types.
To explore the utility of Principal Component Analysis (PCA) for cardiac pattern classification.

Main Methods:

Developed a compression algorithm integrating local extreme extraction, adaptive hysteretic filtering, and Lempel-Ziv-Welch (LZW) coding.
Validated the algorithm using eight common normal and pathological cardiac beat types.
Employed a multi-layer perceptron (MLP) neural network for analyzing original and reconstructed cardiac patterns.
Investigated Principal Component Analysis (PCA) for cardiac pattern classification.

Main Results:

The proposed ECG compression algorithm demonstrated effective data reduction.
The algorithm maintained high signal fidelity, as confirmed by MLP neural network analysis.
Principal Component Analysis (PCA) showed potential for cardiac pattern classification.

Conclusions:

The novel ECG compression algorithm offers a promising approach for efficient cardiac signal management.
The introduced 'quality score' provides a comprehensive measure of compression performance.
Further research into PCA for cardiac classification is warranted.