Scalable serial hardware architecture of multilayer perceptron neural network for automatic wheezing detection

Abstract

This paper proposes a serial hardware architecture of a multilayer perceptron (MLP) neural network for real-time wheezing detection in respiratory sounds. As an established classification tool, the MLP has proven its ability to identify complex patterns within respiratory sounds. The proposed fully serial architecture uses a single calculation unit, independently of the number of neurons in the MLP network. It is also a fully scalable architecture that permits to implement MLP networks, of any size, easily and efficiently without modifying the design or wiring. The proposed serial architecture has been implemented on a low-cost and power-efficient field programmable gate array (FPGA) chip using a high-level programming tool. The respiratory sounds classification rates are evaluated in terms of sensitivity, specificity, performance, and accuracy. The proposed serial architecture reaches the same classification performances as the parallel one, but it presents the main advantage of using much fewer hardware resources.