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

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.

Automated summary

This paper proposes a serial hardware architecture for real-time wheezing detection in respiratory sounds using a multilayer perceptron (MLP) neural network. The proposed architecture is fully scalable and uses a single calculation unit regardless of the number of neurons in the MLP network. It has been implemented on a low-cost and power-efficient FPGA chip using a high-level programming tool and achieves the same classification performances as the parallel architecture but with fewer hardware resources.