FPGA implementation of an automatic wheezes detector based on MFCC and SVM

The present paper proposes a new hardware implementation to categorize lung sounds into wheeze and normal groups. The suggested architecture employs the Mel-Frequency Cepstral Coefficients (MFCC) for feature extraction and the Support Vector Machine (SVM) for classification. In the current study, the SVM parameters are obtained during the training phase using the LIBSVM library in MATLAB, while the testing phase is performed on FPGA. The used database is composed of 12 normal respiratory sounds and 12 respiratory sounds containing wheezes. The classification results obtained with FPGA are compared to those obtained with MATLAB.