Key result
A compressed sensing-based digital signal compression scheme achieved low code rates with extremely low computational complexity at the encoder for ECG signals.
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A compressed sensing-based ECG signal compression scheme offers a low-complexity alternative for resource-constrained wireless sensor nodes, achieving competitive code rates at low ADSNR requirements.
When transmission or storage costs are an issue, lossy data compression enters the processing chain of resource-constrained sensor nodes. However, their limited computational power imposes the use of encoding strategies based on a small number of digital computations. In this case study, we propose the use of an embodiment of compressed sensing as a lossy digital signal compression, whose encoding stage only requires a number of fixed-point accumulations that is linear in the dimension of the encoded signal. We support this design with some evidence that for the task of compressing ECG signals, the simplicity of this scheme is well-balanced by its achieved code rates when its performances are compared against those of conventional signal compression techniques.
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Cambareri et al. (2015) studied ECG signal compression. Compressed Sensing (CS) based Digital Signal Compression vs. Huffman Coding and Set Partitioning In Hierarchical Trees (SPIHT) was evaluated on Code rate and Average Decoded Signal-to-Noise Ratio (ADSNR). A compressed sensing-based digital signal compression scheme achieved low code rates with extremely low computational complexity at the encoder for ECG signals.
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