ECG compression for low-power sensor nodes using sparse frequency spectrum features
Biosignals often require high data transmission in real-time monitoring and visualization. Low-power techniques are always desirable for designing sustainable wireless sensor nodes. Signal compression techniques provide a promising solution in developing low-power wireless sensor nodes as it can significantly reduce the amount of data transmitted via power-demanding wireless transmission and thus greatly lower the energy consumption of sensor nodes. In this study, we develop a new approach for ECG signal compression on low-power ECG sensor nodes by leveraging sparse features of ECG signals in frequency domain. The experimental results show that our method has better compression performance which achieves the average compression ratio (CR) of 65.91 with the comparable RMSE of no more than 5% than the state-of-the-art that can achieve the CR of around 40 with the same level error rate. The promising compression performance of the proposed method provides a feasible solution to achieve ultra-low power consumption for wireless ECG sensor node design.
2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)
ECG compression for low-power sensor nodes using sparse frequency spectrum features.
2018 IEEE Biomedical Circuits and Systems Conference (BioCAS).
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