Complex study on compression of ECG signals using novel single-cycle fractal-based algorithm and SPIHT.

Area of Science:

• Biomedical Engineering
• Signal Processing
• Medical Informatics

Background:

• Electrocardiogram (ECG) signal compression is crucial for efficient telemedicine.
• Existing compression algorithms lack standardization in description, testing, and performance evaluation.
• This variability hinders objective comparison and adoption.

Purpose of the Study:

• To address the lack of standardization in ECG compression algorithms.
• To introduce and rigorously evaluate a novel Single-cycle fractal-based (SCyF) compression algorithm.
• To provide an example of standardized methodology for algorithm evaluation.

Main Methods:

• Developed and implemented the Single-cycle fractal-based (SCyF) compression algorithm.
• Tested SCyF on four diverse ECG databases: CSE, MIT-BIH Arrhythmia, High-frequency signal, and BUT QDB.
• Compared SCyF against a wavelet transform and set partitioning in hierarchical trees (SPIHT) algorithm using 2 efficiency and 12 quality/distortion metrics.

Main Results:

• The SCyF algorithm achieved competitive compression efficiency.
• Performance metrics reached up to an average of 0.4460 bits per sample (bps).
• Signal quality after compression was maintained, with Peak-to-RMS Difference (PRDN) reaching 2.8236%.

Conclusions:

• The SCyF algorithm presents a viable and effective method for ECG signal compression.
• The study demonstrates a standardized framework for evaluating ECG compression techniques.
• SCyF shows promise for improving telemedicine data transmission efficiency and quality.