Comparison between transforms a behavior qualitative analysis of various biomedical signals

This paper aims to compare the behave of different signals when applied to different compression techniques, to test and find the best compression techniques to each different signal, also proving that different signals behave differently in distinct types of compression, the results of this work was satisfactory to prove that different types of compression can be used on signals to achieve better results.

called the electrooculogram. Primary applications are in ophthalmological diagnosis and in recording eye movements. Unlike the electroretinogram, the EOG does not measure response to individual visual stimuli.
To measure eye movement, pairs of electrodes are typically placed either above and below the eye or to the left and right of the eye. If the eye moves from center position toward one of the two electrodes, this electrode "sees" the positive side of the retina and the opposite electrode "sees" the negative side of the retina. Consequently, a potential difference occurs between the electrodes. Assuming that the resting potential is constant, the recorded potential is a measure of the eye's position Brown et al. (2006).

Electromyography
The EMG is the process by which an examiner puts a needle into a particular muscle and study the electrical activity of that muscle, these electrical activities come from the muscle itself no shocks are used to stimulate the muscle, by that is possible to find a muscle who present a particular problem or disease (Weiss et al. 2015).
The excitability of a muscle fibers through neural control represents a major factor in muscle physiology, for that the EMG is a technique concerned with the development recording and analysis of myoelectric signals, the signals taken by this process are formed by physiological variations in the state of muscle fiber membranes, by that some diseases and problems can be detected if the variation don't follow the normal patterns under minor exceptions (Konrad ET AL 2005), there's another way to measure the EMG, is the neurological EMG, by this way electrical shocks are used to stimuli the muscle, but on this work, the kinesiologic EMG will be approached, on this type only the natural response of the muscle are taken as object of study then for that are used to take the signal.

Discrete Cosine Transform
The DCT is a lossy technique, very related to the Discrete Fourier Transform (DFT),it can often reconstruct a precise sequence of only a few DCT coefficients, this property is very useful for applications that require data reduction, precisely the purpose of this work, to explore the reduction of data use in electrocardiogram, (Nguyen et al. 2017). The DCT has four standard variants, for an x-signal of size N and with the kronecker δ, the transformations are defined by the Equation 1, Equation 2, Equation 3 and Equation 4 respectively.

Equation 1
Equation 2 Equation 3 Equation 4 The EMG is the process by which an examiner puts a needle into a particular muscle and study the electrical activity of that muscle, these electrical activities come from the muscle itself no shocks are used to stimulate the muscle, by that is possible to find a muscle A DCT expresses a series of finitely many data points in terms of a sum of cosine functions. Oscillate at different frequencies. DCT has the applications of solving partial differential equations, Chebyshev approximation, audio compression (Raj and Ray2017).

Fast Walsh Hadamard Transform
A DCT expresses a series of finitely many data points in terms of a sum of cosine functions. Oscillate at different frequencies. DCT has the applications of solving partial differential equations, Chebyshev approximation, audio compression (Raj and Ray2017). Research, Society and Development, v. 9, n. 10, e3179108657, 2020 (CC BY 4

Results and Discussion
In the Figure 3(a), are showed an Original EEG C4 to A1 waves mean, that's the signal example used on this work the picture is shown in the time domain to better understanding and in the 3.b is the 10s reading of an ECG.

Figure 3
Source: Author Source: Author Source: Authors.
In the Figure 4, is showed graphically the original and the reconstructed signals who was tested with the DCT, keeping in mind that the reconstructed signals were not identical because of the noise addiction

a-DCT Reconstructed EEG b-DCT Reconstructed ECG
In the Figure 6, are showed the result obtained after the signal passes through the FWHT, being converted into WHT coefficients, or Walsh functions, with that it's possible to use the IFWHT to obtain a reconstructed signal. Source: Authors.

c-ECG WHT
In the Figure 7, is showed graphically the original and the reconstructed signal who was tested with the DWT, graphically it's possible to see a little difference between each signal showing that the DWT is a precise transform.
In the Figure 9(a), are showed an 8x repeated EOG and the WHT coefficients of it, with that it's possible to obtains the Walsh functions using the FWHT, each sample uses on the EOG example, was taken from the right eye readings the same is shown on the Figure   9(b), but with the EMG, also is showed its WHT coefficients. Research, Society and Development, v. 9, n. 10, e3179108657, 2020 (CC BY 4.
In the Figure 10(a) and Figure 10(b), is showed graphically the original and the reconstructed by IDCT signals, showed in the graphic in different colors, exemplified on the legend each sample the original EOG, original EMG and the reconstructed are nearly similar, of course, taking the graphic as basis, that occurs because the DCT have a small loss rate, keeping in mind, the low difference seen in the graphic above.
On the Table 4, are exemplified the results of the statistic methods applied on the DCT EOG reconstructed signal.