Generalized gamma distribution for biomedical signals denoising

Abstract

AbstractA wide range of signs are acquired from the human body called biomedical signs or biosignals, and they can be at the cell level, organ level, or sub-atomic level. Electroencephalogram is the electrical activity from the cerebrum, the electrocardiogram is the electrical activity from the heart, electrical action from the muscle sound signals referred to as electromyogram, the electroretinogram from the eye, and so on. Studying these signals can be so helpful for doctors, and it can help them examine and predict and cure many diseases.However, these signals are often affected by various types of noise. It is important to denoise the signals to get accurate information from them. The denoising process is solved by proposing an entirely novel family of flexible score functions for blind source separation, based on a family of generalized Gamma densities. To blindly extract the independent source signals, we resort to the popular fast independent component analysis (FastICA) approach; to adaptively estimate the parameters of such score functions, we use an efficient method based on maximum likelihood. The results obtained using generalized Gamma densities in our technique are better than those obtained by other distribution functions.