Detection of the quality of vital signals by the Monte Carlo Markov Chain (MCMC) method and noise deleting.

Vital signal renovation plays an important role in a wide range of applications, including signal analysis and diagnosing diseases through it. Therefore, it is salient to get the main content of the vital signal. In this research, a new approach to the problem of noise removal from vital signals is presented based on random optimization through Monte Carlo Markov Chain (MCMC) sampling. For this purpose, the problem of noise omission from the vital signal is described as a Bayesian squared minimization problem, and considering a non-parametric random approach to solve this problem, the Monte Carlo Markov Chain noise omission approach is flexibly adapted to the noise detection domain in vital signals. To test the performance of the proposed method, four types of vital signals have been used: Medical images, ECG electrocardiogram signals, EEG brain signals as well as ENG nerve and muscle signals. The results of the experiments show that the use of sampling technique based on Gaussian distribution and, retrieving the signal based on the weighted average in the selected samples allows a more accurate estimate of the ideal signal. This more accurate estimation minimizes the difference between the actual and the retrieved signals. As a result, in addition to reducing the mean error squares, the signal-to-noise ratio increases.