Compressed Sensing Analog Front-End for Bio-Sensor Applications

Gangopadhyay, Daibashish; Allstot, Emily G.; Dixon, Anna M. R.; Natarajan, Karthik; Sk, Gupta; Allstot, D.J.

doi:10.1109/jssc.2013.2284673

Cited by 150 publications

(58 citation statements)

References 25 publications

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“…Many prior multi-electrode array designs also relied on [30]. The advantage of CS system is that the sensing operation can be efficiently implemented on-chip.…”

Section: Previous On-chip Compression Systemsmentioning

confidence: 99%

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Communication Channel Analysis and Real Time Compressed Sensing for High Density Neural Recording Devices

Zhang

Duncan

Suo

et al. 2016

IEEE Trans. Circuits Syst. I

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Abstract-Next generation neural recording and BrainMachine Interface (BMI) devices call for high density or distributed systems with more than 1000 recording sites. As the recording site density grows, the device generates data on the scale of several hundred megabits per second (Mbps). Transmitting such large amounts of data induces significant power consumption and heat dissipation for the implanted electronics. Facing these constraints, efficient on-chip compression techniques become essential to the reduction of implanted systems power consumption. This paper analyzes the communication channel constraints for high density neural recording devices. This paper then quantifies the improvement on communication channel using efficient on-chip compression methods. Finally, This paper describes a Compressed Sensing (CS) based system that can reduce the data rate by > 10× times while using power on the order of a few hundred nW per recording channel.

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“…Many prior multi-electrode array designs also relied on [30]. The advantage of CS system is that the sensing operation can be efficiently implemented on-chip.…”

Section: Previous On-chip Compression Systemsmentioning

confidence: 99%

“…Analog implementation performs multiplication and accumulation using analog switches and integrators [28] [29] [30]. Digital implementation moves the multiplication and accumulation step to the digital domain after A/D conversion [8] [16] [17].…”

Section: Previous On-chip Compression Systemsmentioning

confidence: 99%

Communication Channel Analysis and Real Time Compressed Sensing for High Density Neural Recording Devices

Zhang

Duncan

Suo

et al. 2016

IEEE Trans. Circuits Syst. I

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“…After the theory of compressed sensing (CS) [6,7] was proposed, many scholars applied it to the dynamic MR reconstruction [5,8,9]. These methods process the original data by downsampling method instead of traditional full-sampling and reduce the computing time.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Magnetic Resonance Imaging Reconstruction Based on Nonconvex Low‐Rank Model

Chen

Yang

Wang

2017

Mathematical Problems in Engineering

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The quality of dynamic magnetic resonance imaging reconstruction has heavy impact on clinical diagnosis. In this paper, we propose a new reconstructive algorithm based on the + model. In the algorithm, the 1 norm is substituted by the norm to approximate the 0 norm; thus the accuracy of the solution is improved. We apply an alternate iteration method to solve the resulting problem of the proposed method. Experiments on nine data sets show that the proposed algorithm can effectively reconstruct dynamic magnetic resonance images.

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“…CS has been widely used to optimize the measurement process of many applications. Particularly, to compress and recover physiological signals in WBSNs since most of them have sparse representations with respect to sparsity bases like wavelets or discrete cosine transform (DCT) [4]. The main advantage of CS over existing techniques is that its compression process requires less computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Fast and efficient signals recovery for deterministic compressive sensing: Applications to biosignals

Ravelomanantsoa¹,

Rabah²,

Rouane³

2015

2015 Conference on Design and Architectures for Signal and Image Processing (DASIP)

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Compressed sensing is a technique that is suitable for compressing and recovering signals having sparse representations in certain bases. Compressed sensing has been widely used to optimize the measurement process of power and bandwidth constrained systems like wireless body sensor network. The central issues with compressed sensing are mainly the construction of measurement matrices and the development of efficient recovery algorithms. In this paper, we proposed a simple and fast recovery algorithm which performed a thresholding in the discrete cosine transform domain. We validated it by recovering electrocardiogram and electromyogram signals taken from the Phyiobank database. The simulation and experimental results have shown that the proposed recovery algorithm was 25 and 12 times faster than orthogonal matching pursuit and stagewise orthogonal matching pursuit, respectively. In addition, depending on the compression ratio, the signal-to-noise ratio of recovered signals were improved up to 2 dB.

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Compressed Sensing Analog Front-End for Bio-Sensor Applications

Cited by 150 publications

References 25 publications

Communication Channel Analysis and Real Time Compressed Sensing for High Density Neural Recording Devices

Communication Channel Analysis and Real Time Compressed Sensing for High Density Neural Recording Devices

Dynamic Magnetic Resonance Imaging Reconstruction Based on Nonconvex Low‐Rank Model

Fast and efficient signals recovery for deterministic compressive sensing: Applications to biosignals

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