Dictionary Learning-Based Multichannel ECG Reconstruction Using Compressive Sensing
- Resource Type
- Periodical
- Authors
- Deka, B.; Kumar, S.; Datta, S.
- Source
- IEEE Sensors Journal IEEE Sensors J. Sensors Journal, IEEE. 22(16):16359-16369 Aug, 2022
- Subject
- Signal Processing and Analysis
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Robotics and Control Systems
Dictionaries
Wireless communication
Electrocardiography
Correlation
Sensors
Machine learning
Compressed sensing
MECG compression
joint compressed sensing
adaptive multiple dictionary learning
WBAN
- Language
- ISSN
- 1530-437X
1558-1748
2379-9153
Multi-channel electrocardiogram (MECG) compression on lightweight wireless body area network (WBAN) is highly challenging for long-term eHealthcare monitoring. Energy consumption involved in wireless transmission poses an obstacle in the implementation of WBAN. MECG is widely used to diagnose cardiovascular diseases (CVDs), which require a considerable time to extract sufficient clinically relevant data from subjects. Since wireless MECG data transmission dominates energy cost and memory consumption of wireless sensor nodes, it is favorable to reduce the data size without any loss of diagnostic information. Compressed sensing (CS) provides efficient encoding schemes for data reduction and energy consumption in wireless transmission. We propose an adaptive multiple dictionary learning-based joint CS model for MECG compression, which exploits spatial correlation and adaptive features existing in MECG signals. We demonstrate performance of the proposed algorithm with the Physikalisch-Technische Bundesanstalt database (PTB) for MECG compression. Results from the proposed scheme can achieve excellent reconstruction quality with fewer measurements than other existing CS-based approaches. It is the most suitable technique for long-term MECG compression using lightweight WBAN devices.