As technology has advanced, data has become more and more important. The more breakthroughs are achieved, the more data is needed to support them. As a result, more storage is required in the system's memory. Compression is therefore required. Before it can be stored, the data must be compressed. To ensure that information is not lost, efficient compression is necessary. This also makes sure that there is no redundancy in the data that is being kept and stored. Compressive sensing has emerged as a new field of compression thanks to developments in sparse optimization. Rather than relying just on compression and sensing formulations, the theory blends the two. The objective of this thesis is to analyze the concept of compressive sensing and to study several reconstruction algorithms. Additionally, a few of the algorithms were put into practice. This thesis also included a model of the ECG, which is vital in determining the health of the heart. For the most part, the ECG is utilized to diagnose heart illness, and a modified synthetic ECG can be used to mimic some of these arrhythmias.

Emotions have significant importance in human life in learning, decision-making, daily interaction, and perception of the surrounding environment. Hence, it has become very essential to detect and recognize a person's emotional states and to build a connection between humans and computers. This process is called brain-computer interaction (BCI) and is a vast field of research in neuroscience. Hence, in the past few years, emotion recognition has gained adequate attention in the research community. In this thesis, an emotion recognition system is designed and analyzed using EEG signals. Several existing feature extraction techniques are studied, analyzed, and implemented to extract features from the EEG signals. An SVM classifier is used to classify the features into various emotional states. Four emotional states are detected, namely, happy, sad, anger, and relaxed state. The model is tested, and simulation results are presented with an interpretation. Furthermore, this study has mentioned and discussed the efficacy of the results achieved. The findings from this study could be beneficial in developing emotion-sensitive technologies, such as augmented modes of communication for severely disabled individuals who are unable to communicate their feelings directly.