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ECG data compression with time-warped polynomials

¹Laboratory for Electronics, Gent, Belgium.

IEEE Transactions on Bio-Medical Engineering

|November 1, 1993

Summary

A novel adaptive compression method for electrocardiograms (ECGs) represents R-R intervals using time-warped polynomials, achieving high-quality signal approximation below 250 bits/s. This technique offers superior noise reduction and error distribution compared to transform-based methods.

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Area of Science:

Biomedical Engineering
Signal Processing
Medical Informatics

Background:

Electrocardiogram (ECG) data compression is crucial for efficient storage and transmission.
Existing compression methods, such as Discrete Cosine Transform (DCT) and Discrete Legendre Transform (DLT), have limitations in compression ratio and noise handling.
Accurate ECG signal processing is essential for reliable diagnosis.

Purpose of the Study:

To introduce a new adaptive compression method for ECG signals.
To evaluate the compression performance and signal quality of the proposed method.
To compare the new method against existing transform-based schemes.

Main Methods:

Representing each R-R interval by an optimally time-warped polynomial.
Achieving high-quality signal approximation at data rates below 250 bits/s.
Analyzing reconstruction errors and noise reduction capabilities.

Main Results:

The proposed method achieves compression rates lower than DCT and DLT schemes.
The method demonstrates reduced sensitivity to QRS detection errors.
It effectively removes white noise, resulting in more uniform reconstruction errors and lower peak errors.
The reconstruction technique is also applicable for adaptive filtering of noisy ECGs.

Conclusions:

The novel adaptive compression method offers significant advantages in ECG data compression.
It provides a high-quality approximation with improved noise handling and error distribution.
This method presents a promising alternative for efficient ECG signal processing and analysis.

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