Generalized gamma distribution for biomedical signals denoising

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