Energy-efficient ECG compression on wireless biosensors via minimal coherence sensing and weighted ℓ₁ minimization reconstruction

IEEE Journal of Biomedical and Health Informatics
Jun ZhangYuanqing Li

Abstract

Low energy consumption is crucial for body area networks (BANs). In BAN-enabled ECG monitoring, the continuous monitoring entails the need of the sensor nodes to transmit a huge data to the sink node, which leads to excessive energy consumption. To reduce airtime over energy-hungry wireless links, this paper presents an energy-efficient compressed sensing (CS)-based approach for on-node ECG compression. At first, an algorithm called minimal mutual coherence pursuit is proposed to construct sparse binary measurement matrices, which can be used to encode the ECG signals with superior performance and extremely low complexity. Second, in order to minimize the data rate required for faithful reconstruction, a weighted ℓ1 minimization model is derived by exploring the multisource prior knowledge in wavelet domain. Experimental results on MIT-BIH arrhythmia database reveals that the proposed approach can obtain higher compression ratio than the state-of-the-art CS-based methods. Together with its low encoding complexity, our approach can achieve significant energy saving in both encoding process and wireless transmission.

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Citations

Apr 12, 2016·Computers in Biology and Medicine·A SinghS Dandapat
Jan 18, 2018·Diagnostics·Mohamed ElgendiRabab Ward
May 27, 2017·Healthcare Technology Letters·Anurag Singh, Samarendra Dandapat
Aug 4, 2020·Journal of Biomedical Informatics·Issam BoukhennoufaSahar Soheilian Esfahani
Sep 4, 2021·Computer Methods and Programs in Biomedicine·Hongpo ZhangZongmin Wang

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