According to the latest research, it is very much clear that in future we require a huge amount of data as modern advancement in communication and signal processing generates a large number of bytes some examples are 5G peak data rate about 300 Mb/sec, an image of black hole required several petabytes to store & in medical signal processing huge amount of data required. So, by these examples, we can easily understand the scarcity of storage in near future. To overcome this problem of data scarcity such type of data compression is required in which the information of the signal will not be degraded. A well-known method is Compressive Sensing which can easily tackle this problem of data compression.

<span>Medical Imaging and scanning technologies are used to provide better resolution of body and tissues. To achieve a better quality Magnetic Resonance (MR) image with a minimum duration of processing time is a tedious task. So our purpose in this paper is to find out a solution that can minimize the reconstruction time of an MRI signal. </span><span>Compressive sensing can be used to accelerate Magnetic Resonance Image (MRI) acquisition by acquiring fewer data through the under-sampling of k-space, so it can be used to minimize the time. But according to the relaxation time, we can further classify the MRI signal into T1, T2, and Proton Density (PD) weighted images. These weighted images represent different signal intensities for different types of tissues and body parts. It also affects the reconstruction process conducted by using the Compressive Sensing Approach. This study is based on finding out the effect of T1, T2, and Proton Density (PD) weighted images on the reconstruction process as well as various image quality parameters like MSE, PSNR, &amp; SSIM also calculated to analyze this effect. Meanwhile, we can analyze how many samples are enough to reconstruct the MR image so the problem associated with time and scanning speed can be reduced up to an extent. In this paper, we got the Structural Similarity Index Measure (SSIM) value up to 0.89 &amp; PSNR value 37.83451 dB at an 85 % compression ratio for the T2 weighted image. </span>

Electrocardiogram (ECG) signal is a bio-electrical activity of the heart. It is a common routine and important cardiac diagnostic tool where in electrical signals are measured and recorded to know the functional status of heart, but ECG signal can be distorted with noise as, various artifacts corrupt the original ECG signal and reduces it quality. Therefore, there is a need to remove such artifacts from the original signal and improve its quality for better interpretation. Digital filters are used to remove noise error from the low frequency ECG signal and improve the accuracy the signal. Noise can be any interference due to motion artifacts or due to power equipment that are present where ECG had been taken. Thus, ECG signal processing has become a prevalent and effective tool for research and clinical practices. This paper presents the comparative analysis of FIR and IIR filters and their performances from the ECG signal for proper understanding and display of the ECG signal.

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