Redundancy cancellation of compressed measurements by QRS complex alignment

Area of Science:

• Biomedical Engineering
• Signal Processing
• Digital Health

Background:

• Continuous long-term healthcare monitoring necessitates efficient data management for on-chip processing.
• Energy consumption is a critical challenge in developing wearable and implantable healthcare devices.
• ElectroCardioGram (ECG) signals possess pseudo-periodic characteristics that can be exploited for data reduction.

Purpose of the Study:

• To develop a data reduction technique for ECG signals to address on-chip energy consumption challenges.
• To leverage the pseudo-periodic nature of ECG signals for efficient data compression.
• To improve the reconstruction quality of compressed ECG data for long-term monitoring applications.

Main Methods:

• Utilizing the pseudo-periodic nature of ECG signals to remove frame redundancy.
• Applying Compressed Sensing (CS) to compress aligned QRS complexes, creating sparse measurement vectors.
• Assessing algorithm efficiency using the standard MIT-BIH database and analyzing power consumption.

Main Results:

• The proposed algorithm achieves superior reconstruction quality compared to state-of-the-art techniques across all compression ratios.
• Aligning ECG frames with a 0.05% R-peak detection error enables greater compression for PRD > 5% with a 5-bit non-uniform quantizer.
• The technique demonstrates a very good recovery performance with only a marginal increase in energy consumption (4.9μW per frame) compared to traditional CS.

Conclusions:

• ECG frame alignment is an effective strategy for enhancing data compression and reconstruction quality in long-term monitoring.
• The proposed method offers a viable solution for reducing on-chip energy consumption in healthcare devices without significant performance compromise.
• This approach contributes to the development of more efficient and effective continuous healthcare monitoring systems.