Compressed sensing reconstruction: Comparative study with applications to ECG bio-signals

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

doi:10.1109/iscas.2011.5937688

Cited by 28 publications

(12 citation statements)

References 6 publications

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“…►The mean of ECG blocks is rounded in the sliding window to the nearest multiple of 2 L , where L is the BSBL level [22]. ►To simulate SNR for ECG signals the following equation is used [23]. 10 20 log (0.01…”

Section: Simulation Resultsmentioning

confidence: 99%

Robust Low-Power Algorithm for Random Sensing Matrix for Wireless ECG Systems Based on Low Sampling-Rate Approach

Balouchestani

Raahemifar

Krishnan

2013

JSIP

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The main drawback of current ECG systems is the location-specific nature of the systems due to the use of fixed/wired applications. That is why there is a critical need to improve the current ECG systems to achieve extended patient's mobility and to cover security handling. With this in mind, Compressed Sensing (CS) procedure and the collaboration of Sensing Matrix Selection (SMS) approach are used to provide a robust ultra-low-power approach for normal and abnormal ECG signals. Our simulation results based on two proposed algorithms illustrate 25% decrease in sampling-rate and a good level of quality for the degree of incoherence between the random measurement and sparsity matrices. The simulation results also confirm that the Binary Toeplitz Matrix (BTM) provides the best compression performance with the highest energy efficiency for random sensing matrix.

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Section: Simulation Resultsmentioning

confidence: 99%

Robust Low-Power Algorithm for Random Sensing Matrix for Wireless ECG Systems Based on Low Sampling-Rate Approach

Balouchestani

Raahemifar

Krishnan

2013

JSIP

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“…In the future, C-coded compressed sensing programs and/or graphics processing units (GPUs) hardware along with GPU-based L1 minimization solver would be exploited to reduce the reconstruction time for the radial undersampling scheme [13][14]. Additionally, adaptive or noise-tolerant compressed sensing algorithms [15][16] would be adopted to address the problem of poor reconstruction performance at low image SNR.…”

Section: Discussionmentioning

confidence: 99%

Simulation-based analysis of the effect of hot spot size, image SNR and undersampling scheme on compressed sensing reconstruction of MR temperature images during HIFU treatment

Chang

Yao

Hwang

et al. 2012

2012 5th International Conference on BioMedical Engineering and Informatics

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Compressed sensing MRI can accelerate MRI temperature monitoring of tissues undergoing the high-intensity focused ultrasound (HIFU) treatment. This paper investigates the relationship between the accuracy of the MRI temperature maps reconstructed using reference image-based compressed sensing and some parameters such as the HIFU hot spot size, the image signal-to-noise ratio (SNR) and the k-space undersampling scheme by a simulation approach. Results indicate that a big HIFU hot spot size almost always helps to reduce the temperature error for Cartesian variable-density undersampling schemes but scarcely influences the reconstruction performance for the radial undersampling scheme. Moreover, the temperature error almost always increases with the image noise level. The radial undersampling scheme outperforms its Cartesian counterparts under the condition of a small HIFU hot spot size, high image SNR and a high degree of undersampling. Our future work will include finding suitable Cartesian undersampling patterns prospectively for our specific HIFU treatment and reducing the reconstruction error at low image SNR as well as the reconstruction time for the radial undersampling scheme.

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“…CS-based systems for EMG and ECG monitoring have been thoroughly investigated, where various aspects have been well analyzed, for instance, the comparison between CS and state-of-the-art compression techniques [7], the system design considerations [5], the effect of the sparsifying dictionaries [8], and the best algorithms in terms of quality of reconstruction [9]. In addition, authors in [10] further leveraged the biosignals structure, where, instead of only exploring the signal sparsity in one domain, the authors proposed using all the available structure such as low rank, piecewise smoothness, and the sparsity in more than one domain.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System

Djelouat

Baali

Amira

et al. 2017

Wireless Communications and Mobile Computing

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The last decade has witnessed tremendous efforts to shape the Internet of things (IoT) platforms to be well suited for healthcare applications. These platforms are comprised of a network of wireless sensors to monitor several physical and physiological quantities. For instance, long-term monitoring of brain activities using wearable electroencephalogram (EEG) sensors is widely exploited in the clinical diagnosis of epileptic seizures and sleeping disorders. However, the deployment of such platforms is challenged by the high power consumption and system complexity. Energy efficiency can be achieved by exploring efficient compression techniques such as compressive sensing (CS). CS is an emerging theory that enables a compressed acquisition using well-designed sensing matrices. Moreover, system complexity can be optimized by using hardware friendly structured sensing matrices. This paper quantifies the performance of a CS-based multichannel EEG monitoring. In addition, the paper exploits the joint sparsity of multichannel EEG using subspace pursuit (SP) algorithm as well as a designed sparsifying basis in order to improve the reconstruction quality. Furthermore, the paper proposes a modification to the SP algorithm based on an adaptive selection approach to further improve the performance in terms of reconstruction quality, execution time, and the robustness of the recovery process.

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Compressed sensing reconstruction: Comparative study with applications to ECG bio-signals

Cited by 28 publications

References 6 publications

Robust Low-Power Algorithm for Random Sensing Matrix for Wireless ECG Systems Based on Low Sampling-Rate Approach

Robust Low-Power Algorithm for Random Sensing Matrix for Wireless ECG Systems Based on Low Sampling-Rate Approach

Simulation-based analysis of the effect of hot spot size, image SNR and undersampling scheme on compressed sensing reconstruction of MR temperature images during HIFU treatment

An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System

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