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Related Concept Videos

Electrocardiogram01:29

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 the T...
ECG Interpretation of Rhythms01:24

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. When...
Correlation between ECG and Cardiac Cycle01:25

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 Fundamentals01:28

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...
Instrumentation Amplifier01:25

Instrumentation Amplifier

An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...
Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...

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Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
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Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

ECG signal compression using compressive sensing and wavelet transform.

Akanksha Mishra1, Falgun Thakkar, Chintan Modi

  • 1Department of Electronics & Communication Engineering, G H Patel College of Eng & Tech., Vallabh Vidyanagar, India. akankshamishra33@yahoo.in

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
PubMed
Summary

Compressed Sensing reconstructs sparse ECG signals efficiently. The reverse biorthogonal wavelet family demonstrates superior performance across various compression ratios, outperforming other wavelet families in signal reconstruction quality.

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Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
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Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

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Last Updated: May 14, 2026

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
08:08

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
06:49

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Data Compression

Background:

  • Compressed Sensing (CS) enables signal reconstruction below the Nyquist rate.
  • Electrocardiogram (ECG) signals exhibit sparsity in the wavelet domain.
  • Wavelet-based CS offers efficient ECG signal compression and reconstruction.

Purpose of the Study:

  • To compare the performance of different wavelet families for ECG signal reconstruction using Compressed Sensing.
  • To evaluate reconstruction quality based on Mean Square Error (MSE), Peak Signal To Noise Ratio (PSNR), Percentage Root Mean Square Difference (PRD), and Correlation Coefficient (CoC).
  • To analyze reconstruction accuracy at compression ratios of 2:1, 4:1, and 6:1.

Main Methods:

  • ECG signals were reconstructed using Compressed Sensing principles.
  • Signal sparsity was exploited in the wavelet domain.
  • L1 minimization was employed as the recovery algorithm.

Main Results:

  • The reverse biorthogonal wavelet family consistently yielded better reconstruction results across all tested compression ratios.
  • Quantitative performance measures (MSE, PSNR, PRD, CoC) confirmed the superiority of the reverse biorthogonal family.
  • Effective ECG signal reconstruction was achieved even at higher compression ratios (e.g., 6:1).

Conclusions:

  • The reverse biorthogonal wavelet family is highly effective for Compressed Sensing-based ECG signal reconstruction.
  • This approach offers a promising method for efficient ECG data compression without significant loss of signal integrity.
  • Wavelet domain sparsity combined with L1 minimization provides robust ECG signal recovery.