An effective low-complexity multi-vital-signs compression technique for embedded-link e-home healthcare

Area of Science:

• Biomedical Engineering
• Signal Processing
• Embedded Systems

Background:

• Home healthcare systems require efficient data handling on resource-limited devices.
• Existing compression methods are too complex for embedded systems.
• Data compression is crucial for mobile health monitoring and diagnosis.

Purpose of the Study:

• To develop a low-complexity data compression technique for multi-vital-signs.
• To adapt the technique for embedded healthcare devices.
• To validate its effectiveness in cardiovascular disease diagnosis.

Main Methods:

• Utilized orthogonal polynomial decomposition (OPD) with Hermite functions.
• Developed a novel low-complexity compression algorithm.
• Optimized parameters for a designated healthcare system.
• Tested with sphygmogram data for cardiovascular disease diagnosis.

Main Results:

• Achieved a compression ratio of up to 6 with less than 2% distortion.
• Preserved significant pathological features for clinical diagnosis.
• Demonstrated robustness with pathological and unusual signals.
• Compressed data is directly usable for medical analysis without decompression.

Conclusions:

• The proposed OPD-based technique is effective for compressing multi-vital-signs data on embedded devices.
• It enables efficient home-use health monitoring and diagnosis.
• The method offers a practical solution for mobile healthcare applications.