tThe pre-processing steps such as filtering, derivatives, and wavelet transform are necessary for manyapplications before data transmission, especially in telemedicine; however, the pre-processing makesvariations on the signal sparsity, entropy, and compression metrics. In this paper, aiming at aninformation-theoretical study, we exemplify pre-processing by Savitzky Golay filtering because of its spe-cial properties, and then, show that (i) adding noise to an ECG signal decreases its sparsity and increasesthe diversity index named Gini-Sympson as a special case of Tsallis entropy; (ii) the sparsity of filtered,and wavelet transformed ECG is increased; (iii) Gini index of the modified signal is not more than thatof the main one, but the non-zero elements are decreased, (iv) the compression metrics such as PRDand CR are improved if the compressed sensing method is performed on the filtered signal. And, finally,theoretical claims are validated by numerical results.

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