Adaptive compression of the ambulatory electrocardiogram

Area of Science:

• Biomedical Engineering
• Signal Processing
• Medical Informatics

Background:

• Ambulatory electrocardiogram (ECG) data compression is crucial for efficient storage and transmission.
• Previous fixed-quantization methods exhibited significant data rate variability (144-230 bps) due to ECG signal fluctuations.
• A need exists for practical ECG storage systems with fixed maximum data rates and minimal distortion.

Purpose of the Study:

• To develop and evaluate an adaptive compression algorithm for ambulatory ECG data.
• To minimize data rate variations while maintaining signal fidelity.
• To improve the efficiency of ECG data storage systems.

Main Methods:

• Modified a previous ECG compression algorithm incorporating average beat subtraction, residual differencing, and Huffman coding.
• Developed two adaptive strategies that adjust quantization step-size based on storage requirements, beat arrival times, and classifications.
• Tested the adaptive algorithm using the MIT/BIH arrhythmia database.

Main Results:

• The more successful adaptive strategy achieved a minimal data rate difference of 0.8 bps across different records.
• The average data rate for the entire database was 193.3 bps.
• Average signal-to-compression noise ratios ranged from 26.82 to 532.83, indicating variable but often high fidelity.

Conclusions:

• Adaptive quantization significantly reduces data rate variability in ECG compression.
• The developed algorithm offers a practical solution for efficient and consistent ECG data storage.
• Further optimization of adaptive strategies can enhance signal-to-noise ratios and overall compression performance.