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Mean-shape vector quantizer for ECG signal compression

J L Cárdenas-Barrera¹, J V Lorenzo-Ginori

¹Electrical Engineering Faculty, Universidad Central de Las Villas, Villa Clara, Cuba.

IEEE Transactions on Bio-Medical Engineering

|January 27, 1999

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Mean-shape vector quantization (MSVQ) offers efficient electrocardiographic (ECG) signal compression. This method achieves high compression ratios exceeding 39, preserving key clinical features for ambulatory monitoring.

Area of Science:

Biomedical Engineering
Signal Processing

Background:

Electrocardiographic (ECG) signal compression is crucial for efficient data handling and storage.
Existing methods may face limitations in achieving high compression ratios while preserving signal integrity.

Purpose of the Study:

To introduce and evaluate a direct waveform mean-shape vector quantization (MSVQ) method for ECG signal compression.
To assess the efficiency, compression ratio, and signal quality of the proposed MSVQ technique.

Main Methods:

Utilizing mean-shape vector quantization (MSVQ) for direct waveform compression of single-lead ECG signals.
Quantizing mean values of ECG segments as scalars and coding residual differences via a vector quantizer.
Applying an entropy encoder to mean and vector codes to enhance compression without quality degradation.

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Main Results:

Achieved high compression ratios (CRs) exceeding 39, with data rates as low as 140 bits per second (bps).
Demonstrated low waveform distortion, preserving clinically significant ECG features.
Analyzed computational complexity, indicating suitability for real-time implementation.

Conclusions:

MSVQ is an effective ECG compression method, balancing high compression with signal fidelity.
The technique is particularly advantageous for ambulatory ECG monitoring due to its efficiency.
Further optimization of parameters like segment duration and codebook size can enhance performance.