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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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The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
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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)graphically represents the heart's electrical activity on ECG paper or a monitor.
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Related Experiment Video

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ECG compression with Douglas-Peucker algorithm and fractal interpolation.

Hichem Guedri1, Abdullah Bajahzar2, Hafedh Belmabrouk3

  • 1Electronics and Microelectronics Laboratory, Physics Department, Faculty of Sciences, Monastir University, Monastir 5019, Tunisia.

Mathematical Biosciences and Engineering : MBE
|July 2, 2021
PubMed
Summary

This study introduces a novel electrocardiogram (ECG) compression technique leveraging fractal geometry. The fractal ECG compression method achieves high compression ratios and preserves signal integrity, offering an efficient solution for ECG data management.

Keywords:
Douglas-Peucker algorithm (DP)Iterated Function System (IFS)compression methodelectrocardiogram (ECG)fractal interpolation

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Fractal Geometry

Background:

  • Electrocardiogram (ECG) signals exhibit fractal characteristics.
  • Efficient compression of ECG data is crucial for storage and transmission.

Purpose of the Study:

  • To propose and evaluate a new ECG compression method based on fractal techniques.
  • To assess the compression ratios and signal fidelity of the proposed method.

Main Methods:

  • ECG signals are converted into a 2-D array.
  • The Douglas-Peucker (DP) algorithm identifies critical points for compression.
  • Fractal interpolation and Iterated Function System (IFS) reconstruct the signal during decompression.

Main Results:

  • The proposed method achieves compression ratios ranging from 3.19 to 27.58.
  • Low Percentage Remaining Difference (PRD) indicates high signal fidelity.
  • Peak Signal-to-Noise Ratio (PSNR) exceeding 40 dB demonstrates adequate retention of detailed ECG structure.

Conclusions:

  • The fractal-based ECG compression method is effective and efficient.
  • The technique successfully balances high compression ratios with minimal data loss.
  • This approach offers a promising alternative for ECG signal compression.