Modified Weibull distribution for Biomedical signals denoising

Abstract

A wide range of signs are acquired from the human body called Biomedical signs or biosignals, 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, it can help them examine and predict and cure many diseases.However, Biomedical signals are often affected by various types of noise, it's 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 modified Weibull densities. To blindly extract the independent source signals, we resort to the popular Fast independent component analysis approach, to adaptively estimate the parameters of such score functions, we use an efficient method based on maximum likelihood. The results obtained using modified Weibull densities in our technique are better than those obtained by other distribution functions.

Keywords

• Biomedical signals denoise
• Modified Weibull distribution
• Maximum likelihood
• Electroencephalogram
• Electrocardiogram
• Source separation