Compressed sensing for bioelectric signals: a review

Area of Science:

• Biomedical Engineering
• Signal Processing
• Data Compression

Background:

• Bioelectric signals (e.g., ECG, EEG) generate large datasets.
• Efficient compression is crucial for storage, transmission, and embedded hardware deployment.
• Compressed Sensing (CS) is an emerging technique for signal acquisition and compression.

Purpose of the Study:

• To comprehensively review Compressed Sensing (CS) applications in bioelectric signal compression.
• To analyze the advantages and disadvantages of CS for various biosignals.
• To evaluate CS suitability for embedded hardware and compare it with existing methods.

Main Methods:

• Review of current Compressed Sensing (CS) trends and techniques.
• Objective quantification of CS performance using metrics like PRD and SNR.
• Comparative analysis of CS against state-of-the-art algorithms for ECG and EEG compression.

Main Results:

• CS demonstrates significant potential for compressing bioelectric signals like ECG and EEG.
• Performance metrics (PRD, SNR) indicate CS's effectiveness, with varying suitability for different biosignals.
• CS shows promise for embedded hardware due to its efficient data acquisition and compression capabilities.

Conclusions:

• Compressed Sensing (CS) is a viable and promising technology for bioelectric signal compression.
• Further research is needed to address technical challenges and optimize CS for diverse biosignal applications.
• Future trends indicate increased adoption of CS in wearable and implantable bioelectronic devices.