Algorithmic Analysis of Optimized Bi-orthogonal Wavelet Filters on Compressed Sensing

In the present advanced world, real-time health observing is turning into a most significant test in the field of medical research. The EEG and ECG are the most broadly utilized signals in the medical field, as they are recognized to be the markers of dreadful diseases. As the EEG signal is being recorded persistently, the amount of signal produced is higher. Thus, the use of the EEG signals in the machine, as well as human communication, is profoundly difficult because of the less recreation quality of the signals. Compressive sensing is recommended as a promising methodology for taking care of this huge information. Thus, this paper proposes a compressed sensing technique for the compression of EEG and ECG signals. This technique is developed with 3 major stages: `stable Measurement Matrix modeling, compression of the signals, and reconstruction of the signal'. Here, the signal compression process plays a major role, and thereby a new working principle is found to be developed inclusive of transformation of the signal, Θ computation, and normalization as well. The evaluation of the theta ( Θ) value is accomplished using an Enhanced bi-orthogonal wavelet filter, whose scaling coefficient is fine-tuned by the LM-CSA. Further, the algorithmic analysis is undergone to the newly developed compressed sensing algorithm to expose its influence over the variation in certain remarkable measures like mutation rate of LA and Awareness probability of CSA for EEG and ECG signal corresponding to diverse Bi-orthogonal wavelets.