Kalman Filters in different biomedical signals-An Overview

Control systems are highly used in various signal processing systems like communication system, biomedical system, etc. to measure the system output in real time. These signals are not easily measured with the presence of noises in the system. Especially, Electroencephalogram (EEG) is a signal used to identify the behavior of the human brain whereas; Electrocardiogram (ECG) is signal useful in studying the function of heart with respect to time. In this review paper, several filtering techniques such as Kalman filter, Adaptive Kalman Filter, Extended Kalman Filter with Multilayer Perceptron (MLP) method to deal with signals and remove the noises. Bayesian framework Method are preferred for use in the system to identify the system parameters for noise calculation and easy to remove the random noises from the system. EKF used to treat weights of the neural network system as state of nonlinear system. The advantage of MLP is that its learning ability; also enhance the execution of EKF filter.