Efficient FPGA-based architecture of an automatic wheeze detector using a combination of MFCC and SVM algorithms

This paper proposes a new hardware implementation of an automatic wheeze detector in pulmonary sounds. The proposed system is based on the combination of Mel-frequency cepstral coefficients (MFCC) feature extraction method and a support vector machine (SVM) classifier. It has been implemented on a field programmable gate array (FPGA) chip using the Xilinx System Generator (XSG) programming tool and the Nexys-4 development board, which is built around the low-cost Artix-7 FPGA device. The LIBSVM library has been used to extract the SVM parameters during the training phase in the Matlab environment, then the MFCC feature extraction and the SVM testing phase are performed on the FPGA chip. Two hardware architectures (default and optimized) of the SVM classifier are implemented and compared in terms of resource utilization, maximum operating frequency, and power consumption for the Artix-7 XC7A100T FPGA Chip. The detection rates obtained by the fixed-point XSG implementation are presented and compared to those obtained by the floating-point Matlab simulation. It has been also compared to existing methods in terms of the detection rates and the characteristics of its implementation.