Compressed sensing of ECG bio-signals using one-bit measurement matrices

Allstot, Emily G.; Chen, Andrew Y.; Dixon, Anna M. R.; Gangopadhyay, Daibashish; Mitsuda, Heather; Allstot, D.J.

doi:10.1109/newcas.2011.5981293

2011 IEEE 9th International New Circuits and Systems Conference 2011

DOI: 10.1109/newcas.2011.5981293

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Compressed sensing of ECG bio-signals using one-bit measurement matrices

Emily G. Allstot

Andrew Y. Chen

Anna M. R. Dixon

et al.

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Cited by 10 publications

(2 citation statements)

References 7 publications

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“…1 shows the architecture of the IEBSM. Compared with the sparse binary matrix (SBM) Φ [3,6], the IESBM Φ′ includes two additional steps in the matrix generation: (i) the AI detection and (ii) the EM. In Fig.…”

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Information‐enhanced sparse binary matrix in compressed sensing for ECG

Luo

Wang

et al. 2014

Electron. lett.

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An information-enhanced sparse binary matrix (IESBM) is proposed to improve the quality of the recovered ECG signal from compressed sensing. With the detection of the area of interest and the enhanced measurement model, the IESBM increases the information entropy of the compressed signal and preserves more information during compression; thus, it guarantees a high-quality recovery. The experimental results indicate that the proposed matrix is suitable for compressed sensing of the ECG signal with small distortions in both overall and the concerned diagnostic segments.Introduction: As a novel sampling paradigm, compressed sensing (CS) combines the sampling and compression into one step. It efficiently collects signals following the 'information rate' instead of the 'Nyquist rate'. This technology has attracted attention from both the industry world and the academic world, particularly in wireless body-area networks.Recently, the CS-enabled wireless electrocardiogram (ECG) nodes have been presented in many works [1][2][3][4]. The advantages of the CS-enabled wireless ECG node include low complexity, low cost, compactness and energy efficiency. There are three key aspects of CS-based ECG node: (i) the sparsity of the ECG signal, (ii) the measurement matrix and (iii) the recovery algorithm. Mamaghanian et al. [1] proposed the CS-based compression framework of wireless ECG transmission; Dixon et al. [2] discussed the CS system considerations for the wireless ECG sensor and Zhang et al. [3, 4], inspired from by correlation structure of the bio-signal, proposed a block sparse Bayesian learning (BSBL) algorithm for ECG CS recovery and obtained good results.Although there are extensive studies on the signal sparsity and the recovery algorithm, there is no report on the study of the measurement matrix for ECG compressed measurement. The signal quality of the recovered ECG, especially the QRS complex, still has room for improvement when some of the reported measurement matrices are used, such as the Bernoulli, 1-bit Toeplitz and 1-bit circulant matrices [2]. To achieve the smallest distortion for cardiovascular diagnosis has motivated us to develop a new measurement matrix for high-quality CS-based ECG application.This Letter presents an information-enhanced sparse binary matrix (IESBM) in CS for the ECG signal. To our knowledge, this is the first report on a custom-designed special measurement matrix for CS-based ECG application. With the detection of the area of interest (AI) and the enhanced measurement (EM) model, the matrix increases the information entropy of the compressed signal and preserves more information during compression, which guarantees high-quality recovery and small distortion of the overall signal as well as the concerned diagnostic ECG segments.

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confidence: 99%

Information‐enhanced sparse binary matrix in compressed sensing for ECG

Luo

Wang

et al. 2014

Electron. lett.

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“…In recent works [1,[7][8][9][10], it has been found that CS-based methods used in ECG sampling devices exhibit the best overall energy efficiency. However, it was reported that they are inferior in terms of compression performance compared with DWT-based compression methods.…”

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On ECG reconstruction using weighted‐compressive sensing

Zonoobi

Kassim

2014

Healthcare Technology Letters

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The potential of the new weighted-compressive sensing approach for efficient reconstruction of electrocardiograph (ECG) signals is investigated. This is motivated by the observation that ECG signals are hugely sparse in the frequency domain and the sparsity changes slowly over time. The underlying idea of this approach is to extract an estimated probability model for the signal of interest, and then use this model to guide the reconstruction process. The authors show that the weighted-compressive sensing approach is able to achieve reconstruction performance comparable with the current state-of-the-art discrete wavelet transform-based method, but with substantially less computational cost to enable it to be considered for use in the next generation of miniaturised wearable ECG monitoring devices.

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Evaluation of Digital Compressed Sensing for Real-Time Wireless ECG System with Bluetooth low Energy

et al. 2016

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In this paper, a wearable and wireless ECG system is firstly designed with Bluetooth Low Energy (BLE). It can detect 3-lead ECG signals and is completely wireless. Secondly the digital Compressed Sensing (CS) is implemented to increase the energy efficiency of wireless ECG sensor. Different sparsifying basis, various compression ratio (CR) and several reconstruction algorithms are simulated and discussed. Finally the reconstruction is done by the android application (App) on smartphone to display the signal in real time. The power efficiency is measured and compared with the system without CS. The optimum satisfying basis built by 3-level decomposed db4 wavelet coefficients, 1-bit Bernoulli random matrix and the most suitable reconstruction algorithm are selected by the simulations and applied on the sensor node and App. The signal is successfully reconstructed and displayed on the App of smartphone. Battery life of sensor node is extended from 55 h to 67 h. The presented wireless ECG system with CS can significantly extend the battery life by 22 %. With the compact characteristic and long term working time, the system provides a feasible solution for the long term homecare utilization.

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Compressed sensing of ECG bio-signals using one-bit measurement matrices

Cited by 10 publications

References 7 publications

Information‐enhanced sparse binary matrix in compressed sensing for ECG

Information‐enhanced sparse binary matrix in compressed sensing for ECG

On ECG reconstruction using weighted‐compressive sensing

Evaluation of Digital Compressed Sensing for Real-Time Wireless ECG System with Bluetooth low Energy

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