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Related Concept Videos

Electrocardiogram01:29

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.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
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ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

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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....
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Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

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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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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Upsampling01:22

Upsampling

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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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Downsampling01:20

Downsampling

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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
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Related Experiment Video

Updated: Jul 20, 2025

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism
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Electrocardiogram signal compression using adaptive tunable-Q wavelet transform and modified dead-zone quantizer.

Hardev Singh Pal1, A Kumar1, Amit Vishwakarma1

  • 1Discipline of Electronics and Communication Engineering, PDPM Indian Institute ofInformation Technology, Design and Manufacturing Jabalpur, Jabalpur 482005, India.

ISA Transactions
|July 31, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient electrocardiogram (ECG) compression algorithm using adaptive tunable-Q wavelet transform and Sparse-grey wolf optimization. The method significantly reduces data size for telemedicine while preserving signal integrity.

Keywords:
Adaptive tunable-Q wavelet transformDead-zone quantizerECG signal compressionOptimization algorithmsRun-length encodingSparse-GWO

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Data Compression

Background:

  • Electrocardiogram (ECG) signals are crucial for diagnosing heart conditions.
  • Large ECG data volumes pose storage and bandwidth challenges in telemedicine.
  • Efficient compression algorithms are needed to manage ECG data effectively.

Purpose of the Study:

  • To propose a novel, efficient ECG signal compression algorithm.
  • To optimize compression parameters using a novel metaheuristic algorithm.
  • To evaluate the algorithm's performance against existing methods.

Main Methods:

  • Adaptive tunable-Q wavelet transform (TQWT) for signal decomposition.
  • Modified dead-zone quantizer (DZQ) for thresholding and quantization.
  • Sparse-grey wolf optimization (Sparse-GWO) for parameter optimization.
  • Run-length encoding (RLE) for efficient data encoding.

Main Results:

  • The proposed algorithm achieved a compression ratio (CR) of 20.56.
  • Maintained high signal quality with a percent root-mean-square difference (PRD1) of 3.21% and signal-to-noise ratio (SNR) of 30.62 dB.
  • Sparse-GWO demonstrated reduced computation time compared to original GWO and PSO variants.
  • Quality score (QS1) averaged 7.79, indicating minimal impact on ECG morphology.

Conclusions:

  • The novel ECG compression algorithm offers high compression ratios with minimal signal distortion.
  • Sparse-GWO provides an efficient method for optimizing compression parameters.
  • The developed algorithm is suitable for telemedicine applications requiring efficient ECG data handling.