Multi-structural signal recovery for biomedical compressive sensing

IEEE Transactions on Biomedical Engineering

Volumn 60, Issue 10, 2013, Pages 2794-2805

  Liu, Yipeng ^a   De Vos, Maarten ^b   Gligorijevic, Ivan ^a   Matic, Vladimir ^a   Li, Yuqian ^c   Van Huffel, Sabine ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b UNIVERSITY OF OLDENBURG   (Germany)

^c UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

Biomedical signal reconstruction; compressive sensing (CS); low rank; piecewise smoothness; sparsity

Indexed keywords

COMPRESSIVE SENSING; CONVEX PROGRAMMING PROBLEMS; LOW RANK; NUMERICAL EXPERIMENTS; PIECEWISE SMOOTHNESS; RECONSTRUCTION ACCURACY; SPARSITY; UNDERDETERMINED SYSTEMS;

BIOELECTRIC PHENOMENA; NUMERICAL METHODS;

SIGNAL RECONSTRUCTION;

ARTICLE; BIOMEDICAL COMPRESSIVE SENSING; ELECTROCARDIOGRAPHY; ELECTROMYOGRAPHY; INFORMATION PROCESSING; MEASUREMENT; NEUROPATHY; NUCLEAR MAGNETIC RESONANCE IMAGING; SIGNAL PROCESSING;

ALGORITHMS; ANIMALS; COMPUTER SIMULATION; DATA COMPRESSION; HUMANS; MODELS, BIOLOGICAL; MONITORING, PHYSIOLOGIC;

EID: 84884583935     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2013.2264772     Document Type: Article

References (41)

1. C. Bachmann, M. Ashouei, V. Pop, M. Vidojkovic, H. De Groot, and B. Gyselinckx, "Low-power wireless sensor bodes for ubiquitous longterm biomedical signal monitoring," IEEE Commun. Mag., vol. 50, no. 1, pp. 20-27, Jan. 2012.
2. M. Lustig, D. Donoho, and J. M. Pauly, "Sparse MRI: The application of compressed sensing for rapid MR imaging," Magn. Reson. Med., vol. 58, no. 6, pp. 1182-1195, Dec. 2007. (Pubitemid 350234205)
3. M. Lustig, D.L. Donoho, J. M. Santos, and J. M. Pauly, "Compressed sensingMRI," IEEE Signal Process. Mag., vol. 25, no. 2, pp. 72-82, Mar. 2008.
4. Y. Guo, S. Ruan, J. Landre, and J. Constans, "A sparse representation method formagnetic resonance spectroscopy quantification," IEEE Trans. Biomed. Eng., vol. 57, no. 7, pp. 1620-1627, Jul. 2010.
5. E. J. Candes and M. B. Wakin, "An introduction to compressive sampling," IEEE Signal Process. Mag., vol. 25, no. 2, pp. 21-30, Mar. 2008.
6. V. Chandrasekaran, B. Recht, P. A. Parrilo, and A. S. Willsky, "The convex algebraic geometry of linear inverse problems," in Proc. 48th Annu. Allerton Conf. Commun. Contr. Comput., Allerton, IL, USA, Sep./Oct. 2010, pp. 699-703.
7. V. Chandrasekaran, "Convex optimization methods for graphs and statistical modeling" Ph.D. dissertation, Massachusetts Inst. Technol., MA, USA, Apr. 2011.
8. J. Romberg, "Imaging via compressive sampling," IEEE Signal Process. Mag., vol. 25, no. 2, pp. 14-20, Mar. 2008.
9. M. Fazel, "Matrix rank minimization with applications" Ph.D. dissertation, Electr. Eng. Dept., Stanford Univ., CA, USA, Mar. 2002.
10. E. J. Candes and Y. Plan, "Matrix completion with noise," Proc. IEEE, vol. 98, no. 6, pp. 925-936, Jun. 2010.
11. A. J. Casson and E. Rodriguez-Villegas, "Signal agnostic compressive sensing for body area networks: Comparison of signal reconstructions," in Proc. IEEE 34th Int. Conf. Eng. Med. Biol. Soc., San Diego, CA, USA, Aug.-Sep. 2012, pp. 4497-4500.
12. S. Aviyente, "Compressed sensing framework for EEG compression," in Proc. IEEE/SP 14th Workshop Stat. Signal Process., Aug. 2007, pp. 181-184.
13. V. Matic, M. DeVos, B. Mijovic, and S.Van Huffel, "Sparse approximation of the neonatal EEG," in Proc. 4thWorkshop Signal Process. Adapt. Sparse Struct. Represent., Edinburgh, U.K., Jun. 2011.
14. H. Mamaghanian, N. Khaled, D. Atienza, and P. Vandergheynst, "Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes," IEEE Trans. Biomed. Eng., vol. 58, no. 9, pp. 2456-2466, Sep. 2011.
15. P. S. Addison, "Wavelet transforms and the ECG: A review," Physiol. Meas., vol. 26, no. 5, pp. 155-199, Aug. 2005.
16. A. Salman, E. G. Allstot, A. Y. Chen, A. M. R. Dixon, D. Gangopadhyay, and D. J. Allstot, "Compressive sampling of EMG bio-signals," in Proc. IEEE Int. Symp. Circuits Syst., May 2011, Brazil, pp. 2095-2098.
17. A. M. R. Dixon, E. G. Allstot, D. Gangopadhyay, andD. J.Allstot, "Compressed sensing system considerations for ECG and EMG wireless biosensors," IEEE Trans. Biomed. Circuits Syst., vol. 6, no. 2, pp. 156-166, May 2012.
18. M. J. Zwarts and D. F. Stegeman, "Multichannel surface EMG: Basic aspects and clinical utility," Muscle Nerve, vol. 28, no. 1, pp. 1-17, Jul. 2003. (Pubitemid 36775954)
19. Z. Zhang, T. Jung, S. Makeig, and B. D. Rao, "Compressed sensing ofEEG for wireless telemonitoring with low energy consumption and inexpensive hardware," IEEE Trans. Biomed. Eng., vol. 60, no. 1, pp. 221-224, Jan. 2013.
20. Y. Liu, I. Gligorijevic, V. Matic, M. De Vos, and S. Van Huffel, "Multisparse signal recovery for compressive sensing," in Proc. 34th Annu. Int. Conf. Eng. Med. Biol. Soc., San Diego, CA, USA, Aug./Sep. 2012, pp. 1053-1056.
21. J. Laska, S. Kirolos, M. F. Duarte, T. S. Ragheb, R. G. Baraniuk, and Y. Messoud, "Theory and implementation of an analog-to-information converter using random demodulation," in Proc. IEEE Int. Symp. Circuits Syst., New Orleans, LI, USA, May 2007, pp. 1959-1962. (Pubitemid 47448919)
22. R. Baraniuk, M. Davenport, R. DeVore, and M. Wakin, "A simple proof of the restricted isometry property for random matrices," Constr. Approx., vol. 28, no. 3, pp. 253-263, Dec. 2008.
23. J. A. Tropp and A. C. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Trans. Inf. Theory, vol. 53, no. 12, pp. 4655-4666, Dec. 2007.
24. A. Cohen, W. Dahmen, and R. Devore, "Compressed sensing and best k-term approximation," J. Amer. Math. Soc., vol. 22, no. 1, pp. 211-231, Jan. 2009.
25. G. Tang and A. Nehorai, "Performance analysis of sparse recovery based on constrained minimal singular values," IEEE Trans. Signal Process., vol. 59, no. 12, pp. 5734-5745, Dec. 2011.
26. S. Nam, M. E. Davies, M. Elad, and R. Gribonval, "Cosparse analysis modeling-uniqueness and algorithms," in Proc. Int. Conf. Acoust. Speech, Signal Process., Prague, Czech Republic, May 2011, pp. 5804-5807.
27. I. Gligorijevic, J. Van Dijk, B. Mijovic, S. Van Huffel, J. Blok, and M. De Vos, "A new and fast approach towards sEMG decomposition," Edical Biol. Eng. Comput., vol. 51, no. 5, pp. 593-605, May 2013.
28. N. Hurley and S. Rickard, "Comparing measures of sparsity," IEEE Trans. Inf. Thoer., vol. 55, no. 10, pp. 4723-4741, Oct. 2009.
29. J. A. Tropp and S. J. Wright, "Computational methods for sparse solution of linear inverse problems," Proc. IEEE, vol. 98, no. 6, pp. 948-958, Jun. 2010.
30. S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, "Distributed optimization and statistical learning via the alternating direction method of multipliers," Found. Trends Mach. Learn., vol. 3, no. 1, pp. 1-122, Jan. 2011.
31. Y. Liu and Q.Wan, "Robust beamformer based on total variationminimisation and sparse-constraint," Electron. Lett., vol. 46, no. 25, pp. 1697-1699, Dec. 2010.
32. D. Needell, and R. Ward, "Near-optimal compressed sensing guarantees for total variation minimization," arXiv:1210.3098, Oct. 2012. [Online]. Available: http://arxiv.org/abs/1210.3098.
33. S. Boyd and L. Vanderberghe, Convex Optimization. New York, NY, USA: Cambridge Univ. Press, 2004.
34. M. Stojnic, F. Parvaresh, and B. Hassibi, "On the reconstruction of blocksparse signals with an optimal number of measurements," IEEE Trans. Signal Process., vol. 57, no. 8, pp. 3075-3085, Aug. 2009.
35. Y. C. Eldar, P. Kuppinger, and H. Bolcskei, "Block-sparse signals: Uncertainty relations and efficient recovery," IEEE Trans. Signal Process., vol. 58, no. 6, pp. 3042-3054, Jun. 2010.
36. B. Hunyadi, M. Signoretto, S. Van Huffel, J. Suykens, andM. De Vos, "Incorporating structural information from the multichannel EEG improves patient-specific seizure detection," Clin. Neurophysiol., vol. 123, no. 12, pp. 2352-2361, Dec. 2012.
37. M. De Vos, D. Nion, S. Van Huffel, and L. De Lathauwer, "A combination of parallel factor and independent component analysis," Signal Process., vol. 92, no. 12, pp. 2990-2999, Jul. 2012.
38. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction, (Series in Statistics).. New York, NY, USA: Springer, 2001.
39. A. Goldberger, L. Amaral, L. Glass, J. Hausdorff, P. Ivanov, R. Mark, J. Mietus, G. Moody, C. Peng, andH. Stanley, "PhysioBank, Physiotoolkit, and Physionet: Components of a new research resource for complex physiologic signals," Circulation, vol. 101, no. 23, pp. 215-220, Jun. 2000.
40. R. Bousseljot, D. Kreiseler, and A. Schnabel, "Nutzung der EKGSignaldatenbank CARDIODAT der PTB ber das Internet," Biomed. Tech. Biomed. Eng., vol. 40, no. 1, pp. 317-318, Jul. 2009.
41. S. G. Lingala, Y. Hu, E. DiBella, and M. Jacob, "Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR," IEEE Trans. Med. Imag., vol. 30, no. 5, pp. 1042-1054, May 2011.