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Erschienen in:
Challenges and Opportunities in Wearable Biomedical Interfaces Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

3. Introduction to Compressive Sampling (CS)

verfasst von : Venkata Rajesh Pamula, Chris Van Hoof, Marian Verhelst

Erschienen in: Analog-and-Algorithm-Assisted Ultra-low Power Biosignal Acquisition Systems

Verlag: Springer International Publishing

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Abstract

This chapter provides an overview of compressive sampling (CS), introducing both the signal acquisition and reconstruction protocols. A novel, computationally light, overlapped window reconstruction algorithm is introduced to circumvent the problem of edge artifacts in conventional CS reconstruction. The proposed approach is shown to reduce the central processing unit (CPU) execution time by a factor of 2.4 without degradation of reconstruction accuracy compared to a traditional longer window reconstruction approach for photoplethysmogram (PPG) signals. Finally, this chapter also presents the state-of-the-art CS implementations for biosignal acquisition and processing.

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Vorheriges Kapitel Adaptive Sampling for Ultra-Low-Power Electrocardiogram (ECG) Readouts
Nächstes Kapitel Compressed Domain Feature Extraction
Fußnoten
1
Strictly speaking restricted isometry property (RIP) of Φ Ψ−1.
 
2
This is true for analog implementations of CS. Digital implementations acquire signal at Nyquist rate (f s,N).
 
3
For perfect reconstruction, X′ = X and S′ = S.
 
4
In analog implementations of CS, only M samples are acquired.
 
5
Assuming Bernoulli measurement matrix.
 
6
The overlapping window overlaps the original window by 50%.
 
7
Random sampling is equivalent to multiplying the signal with a reduced order identity matrix.
 
Literatur
27.
E. Candes, J. Romberg, T. Tao, Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52(2), 489–509 (2006)MathSciNetCrossRef
28.
37.
38.
H. Mamaghanian, N. Khaled, D. Atienza, P. Vandergheynst, Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes. IEEE Trans. Biomed. Eng. 58(9), 2456–2466 (2011)CrossRef
52.
D. Gangopadhyay, E.G. Allstot, A.M.R. Dixon, K. Natarajan, S. Gupta, D.J. Allstot, Compressed sensing analog front-end for bio-sensor applications. IEEE J. Solid State Circuits 49(2), 426–438 (2014)CrossRef
53.
Y. Li, D. Zhao, W.A. Serdijn, A sub-microwatt asynchronous level-crossing ADC for biomedical applications. IEEE Trans. Biomed. Circuits Syst. 7(2), 149–157 (2013)CrossRef
55.
C. Weltin-Wu, Y. Tsividis, An event-driven clockless level-crossing ADC with signal-dependent adaptive resolution. IEEE J. Solid State Circuits 48(9), 2180–2190 (2013)CrossRef
56.
B. Schell, Y. Tsividis, A continuous-time ADC/DSP/DAC system with no clock and with activity-dependent power dissipation. IEEE J. Solid State Circuits 43(11), 2472–2481 (2008)CrossRef
57.
M. Trakimas, S.R. Sonkusale, An adaptive resolution asynchronous ADC architecture for data compression in energy constrained sensing applications. IEEE Trans. Circuits Syst. I Regul. Pap. 58(5), 921–934 (2011)MathSciNetCrossRef
58.
E.J. Candès, M.B. Wakin, An introduction to compressive sampling. IEEE Signal Process Mag. 25(2), 21–30 (2008)CrossRef
59.
D.L. Donoho, Y. Tsaig, Fast solution of l1-norm minimization problems when the solution may be sparse. IEEE Trans. Inf. Theory 54(11), 4789–4812 (2008)CrossRef
60.
61.
R. Tibshirani, Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B Methodol. (1996), pp. 267–288MathSciNetMATH
62.
W. Lu, N. Vaswani, Modified basis pursuit denoising(modified-BPDN) for noisy compressive sensing with partially known support, in 2010 IEEE International Conference on Acoustics, Speech and Signal Processing (Institute of Electrical and Electronics Engineers (IEEE), Piscataway, 2010), pp. 3926–3929CrossRef
63.
M.A.T. Figueiredo, R.D. Nowak, S.J. Wright, Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems. IEEE J. Sel. Top. Sign. Proces. 1(4), 586–597 (2007)CrossRef
64.
A.M.R. Dixon, E.G. Allstot, D. Gangopadhyay, D.J. Allstot, Compressed sensing system considerations for ECG and EMG wireless biosensors. IEEE Trans. Biomed. Circuits Syst. 6(2), 156–166 (2012)CrossRef
65.
F. Ren, D. Marković, 18.5 A configurable 12-to-237kS/s 12.8 mW sparse-approximation engine for mobile ExG data aggregation, in 2015 IEEE International Solid-State Circuits Conference-(ISSCC) Digest of Technical Papers (IEEE, Piscataway, 2015), pp. 1–3
66.
P. Maechler, C. Studer, D.E. Bellasi, A. Maleki, A. Burg, N. Felber, H. Kaeslin, R.G. Baraniuk, VLSI design of approximate message passing for signal restoration and compressive sensing. IEEE J. Emerging Sel. Top. Circuits Syst. 2(3), 579–590 (2012)CrossRef
67.
P. Maechler, P. Greisen, B. Sporrer, S. Steiner, N. Felber, A. Burg, Implementation of greedy algorithms for LTE sparse channel estimation, in 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers (Nov 2010)
68.
V.R. Pamula, M. Verhelst, C. Van Hoof, R.F. Yazicioglu, Computationally-efficient compressive sampling for low-power pulseoximeter system, in 2014 IEEE Biomedical Circuits and Systems Conference (BioCAS) Proceedings (IEEE, Piscataway, 2014), pp. 69–72
69.
V.R. Pamula, System and method for compressed sensing, U.S. Patent App. 14/886,537, 21 Apr 2016
70.
M. Mangia, R. Rovatti, G. Setti, Rakeness in the design of analog-to-information conversion of sparse and localized signals. IEEE Trans. Circuits Syst. Regul. Pap. 59(5), 1001–1014 (2012)MathSciNetCrossRef
71.
S. Mallet, A Wavelet Tour of Signal Processing (Academic Press, Cambridge, 2009)
72.
H. Mamaghanian, N. Khaled, D. Atienza, P. Vandergheynst, Design and exploration of low-power analog to information conversion based on compressed sensing. IEEE J. Emerging Sel. Top. Circuits Syst. 2(3), 493–501 (2012)CrossRef
73.
M. Shoaran, M.H. Kamal, C. Pollo, P. Vandergheynst, A. Schmid, Compact low-power cortical recording architecture for compressive multichannel data acquisition. IEEE Trans. Biomed. Circuits Syst. 8(6), 857–870 (2014)CrossRef
74.
F. Chen, A.P. Chandrakasan, V.M. Stojanovic, Design and analysis of a hardware-efficient compressed sensing architecture for data compression in wireless sensors. IEEE J. Solid State Circuits 47(3), 744–756 (2012)CrossRef
75.
Y. Suo, J. Zhang, T. Xiong, P.S. Chin, R. Etienne-Cummings, T.D. Tran, Energy-efficient multi-mode compressed sensing system for implantable neural recordings. IEEE Trans. Biomed. Circuits Syst. 8(5), 648–659 (2014)CrossRef
76.
W. Guo, N. Sun, A 9.8b-ENOB 5.5fj/step fully-passive compressive sensing SAR ADC for WSN applications, in 2016 46th European Solid-State Device Research Conference (ESSDERC) (Sep 2016)
77.
P.V. Rajesh, J.M. Valero-Sarmiento, L. Yan, A. Bozkurt, C. Van Hoof, N. Van Helleputte, R.F. Yazicioglu, M. Verhelst, A 172μW compressive sampling photoplethysmographic readout with embedded direct heart-rate and variability extraction from compressively sampled data, in 2016 IEEE International Solid-State Circuits Conference (ISSCC) (IEEE, Piscataway, 2016), pp. 386–387
78.
M. Trakimas, R. D’Angelo, S. Aeron, T. Hancock, S. Sonkusale, A compressed sensing analog-to-information converter with edge-triggered SAR ADC core. IEEE Trans. Circuits Syst. I, Reg. Pap. 60(5), 1135–1148 (2013)MathSciNetCrossRef
79.
M. Lustig, D. Donoho, J.M. Pauly, Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn. Reson. Med. 58(6), 1182–1195 (2007)CrossRef
80.
T. Moy, L. Huang, W. Rieutort-Louis, C. Wu, P. Cuff, S. Wagner, J.C. Sturm, N. Verma, An EEG acquisition and biomarker-extraction system using low-noise-amplifier and compressive-sensing circuits based on flexible, thin-film electronics. IEEE J. Solid State Circuits 52(1), 309–321 (2017)CrossRef
81.
B. Hu, F. Ren, Z.-Z. Chen, X. Jiang, M.-C. Chang, 9-bit time–digital-converter-assisted compressive-sensing analogue–digital-converter with 4 GS/s equivalent speed. Electron. Lett. 52(6), 430–432 (2016)CrossRef
82.
D.E. Bellasi, L. Benini, Energy-efficiency analysis of analog and digital compressive sensing in wireless sensors. IEEE Trans. Circuits Syst. Reg. Pap. 62(11), 2718–2729 (2015)MathSciNetCrossRef
83.
D. Bortolotti, A. Bartolini, L. Benini, V.R. Pamula, N. Van Helleputte, C. Van Hoof, M. Verhelst, T. Gemmeke, R.B. Lopez, G. Ansaloni et al., PHIDIAS: ultra-low-power holistic design for smart bio-signals computing platforms, in Proceedings of the ACM International Conference on Computing Frontiers (ACM, New York, 2016), pp. 309–314
84.
J. Zhang, Y. Suo, S. Mitra, S.P. Chin, S. Hsiao, R.F. Yazicioglu, T.D. Tran, R. Etienne-Cummings, An efficient and compact compressed sensing microsystem for implantable neural recordings. IEEE Trans. Biomed. Circuits Syst. 8(4), 485–496 (2014)CrossRef
85.
P.K. Baheti, H. Garudadri, S. Majumdar, Blood oxygen estimation from compressively sensed photoplethysmograph, in Wireless Health 2010, ser. WH ’10 (ACM, New York, 2010), pp. 10–14
Metadaten
Titel
Introduction to Compressive Sampling (CS)
verfasst von
Venkata Rajesh Pamula
Chris Van Hoof
Marian Verhelst
Copyright-Jahr
2019
Buch
Analog-and-Algorithm-Assisted Ultra-low Power Biosignal Acquisition Systems

Print ISBN: 978-3-030-05869-2

Electronic ISBN: 978-3-030-05870-8

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-05870-8_3

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