Fetal ECG extraction using an FIR neural network

Non-invasive electrocardiography reveals itself as a very interesting method to obtain reliable information about the state of the fetus, thus assuring its well-being during pregnancy. In this paper, a finite impulse response (FIR) neural network is included in the familiar adaptive noise cancellation scheme in order to provide highly nonlinear dynamic capabilities to the recovery model. A novel methodology for selecting the optimal topology is also presented. Results from its application to both simulated and real registers are shown and benchmarked with the classical LMS (least mean squares) and normalized LMS (NLMS) algorithms. Outcomes indicate that the FIR network is a reliable method for the fetal electrocardiogram recovery.