Neural networks in ballistocardiography (BCG) using FPGAs

Artificial neural networks have shown good capabilities in medical diagnostic applications. They offer the advantage that they are able to learn the representation by examples, which is of great benefit when the nature of the process is unknown or is difficult to characterise. On the other hand, the hardware implementation of the parallel network structure can dramatically improve the network efficiency. Here, a hardware implementation of neural network based ballistocardiogram (BCG) classification system with field programmable gate arrays (FPGAs) technology is presented. The specific trained neural network is implemented in Xilinx XC4000 series FPGAs