A Case Study in Low-Complexity ECG Signal Encoding: How Compressing is Compressed Sensing?

Cambareri, Valerio; Mangia, Mauro; Pareschi, Fabio; Rovatti, Riccardo; Setti, Gianluca

doi:10.1109/lsp.2015.2428431

Cited by 34 publications

(14 citation statements)

References 27 publications

(29 reference statements)

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“…Note that CR is also dependent on number of bits in the digitized samples (x) and the compressed measurements (y). Detail studies on reconstruction quality with respect to the number of bits in the compressed measurement can be found in [8], [33] and [43]. These results have shown that the resolution of the compressed measurement does not affect the overall reconstruction quality significantly at low M .…”

Section: A Learning a Dictionarymentioning

confidence: 91%

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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Section: A Learning a Dictionarymentioning

confidence: 91%

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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“…It is a recent technique based on sparse data representation. The idea is to bypass the Nyquist-Shannon theorem by reconstructing a signal from fewer samples of data than required [3][4][5].…”

Section: Compressed Sensingmentioning

confidence: 99%

Comparison of Compression Solutions for Impedance and Field Potential Signals of Cardiomyocytes

Guyot

Batista

Djermoune

et al. 2017

Computing in Cardiology Conference (CinC)

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We present in this paper an extensive comparison of compression methods adapted to impedance and field potential signals of cardiomyocytes. Different combinations of the traditional scheme of lossy compression have been tested and other original methods such as compressed sensing were implemented as well. All algorithms are assessed on several criteria such as compression ratio, distortion of the data, etc. We show that the selected method presents the ability and reliability to compress sensitive data with a compression ratio greater than 5:1 while preserving the relevant information content of the recorded data.

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“…Accuracy required for the quantization of y is still an open problem in CS theory [14]. In standard CS, it is known that measurements, due to the central limit theorem, have a zero-mean Gaussian distribution, whose standard deviation is √ n times that of x.…”

Section: Approachmentioning

confidence: 99%

Countering the false myth of democracy: Boosting compressed sensing performance with maximum-energy approach

Mangia

Pareschi

Rovatti

et al. 2017

2017 IEEE International Symposium on Circuits and Systems (ISCAS)

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Compressed Sensing (CS) is an effective way to sample a signal at a sub-Nyquist rate, i.e., by using a number of measurements smaller than the number of samples required when using the standard Nyquist approach. Measurements are obtained as linear projections of input signals along random sensing vectors. CS has been often regarded as a democratic method, in the sense that each measurement contributes to signal reconstruction with a similar amount of information.In this paper, by combining empirical observations with results from recent papers, we propose a different point of view, and show that CS is an oligarchic approach where performance is basically set by the measurements with the highest energy. This allows us to propose a new CS-based approach that bases the reconstruction on the maximumenergy measurements only and improves the compression performance with respect to classical approaches.

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A Case Study in Low-Complexity ECG Signal Encoding: How Compressing is Compressed Sensing?

Cited by 34 publications

References 27 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

Comparison of Compression Solutions for Impedance and Field Potential Signals of Cardiomyocytes

Countering the false myth of democracy: Boosting compressed sensing performance with maximum-energy approach

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