Compressed Sensing (CS) is a novel theory proposed by David Donoho [1] and permits to reduce the number of samples to digitize an analog signal. CS is based on two principles [2]-[4]: a) sparsity, only a few samples are relevant in some bases, and b) incoherence, sampling process has the les correlation with the bases.

S. D. A. Vázquez and D. E. T. Romero are developed 4 apps in Matlab to show how does CS works in 4 different methods for ECG signal:

Method 1 - CS by software: This method is developed in Matlab.

If you are interested in obtain the apps, please click the corresponding buttton. In case of don't work, plaease fill the next form to send you the app via mail:

[1] D. L. Donoho, "Compressed sensing," IEEE Transactions on Information Theory, vol. 52, no. 4, pp. 1289 - 1309, April 2006.

[2] E. J. Candés, J. Romberg and T. Tao, "Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information," IEEE Transactions on Information Theory, vol. 52, no. 2, pp. 489 - 509, February 2006.

[3] D. L. Donoho, M. Elad and V. N. Temlyakov, "Stable recovery of sparse overcomplete representations in the presence of the noise," IEEE Transactions on Information Theory, vol. 52, no. 1, pp. 6 - 18, January 2006.

[4] J. A. Tropp, "Juste relax: convex programming methods for identifying sparse signals in noise," IEEE Transactions on Information Theory, vol. 53, no. 3, pp. 1030 - 1051, March 2006.

[5] F. Chen, A. P. Chandrakasan and V. M. Stojanović, "Design and analysis of a hardware-efficient compressed sensing architecture for data compression in wireless sensors," IEEE Journal of Solid-State Circuits, vol. 47, no. 3, pp. 744 - 756, March 2012.

[6] S. D. A. Vázquez, D. E. T. Romero and M. S. Ramírez, "Digital architecture based on chaotic cellular automata for compressed sensing of electrocardiographic (ECG) signals," in 2018 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC 2018), ACCEPTED.