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Cardiopulmonary acoustic events classification

The acoustic cardiopulmonary signals contain complex but quantifiable essential diagnostic signs. Particularly, the lung sounds associated with inhalation and exhalation, and the heart sounds as particularly characterized by their principal components S1 and S2 when quantitatively utilized could improve the classification accuracy of diverse disease conditions. This project is related to the events classification (inhalation, exhalation, S1 and S2) with Gaussian Mixture Models (GMM) representing states in Hidden Markov Models (HMM), which were further enhanced by applying Principal Component Analysis (PCA) on Mel Frequency Cepstral Coefficients (MFCC) and Quartiles acoustic vectors. These classification outcomes drive the implementation of events quantification techniques, and also the implementation of automated auscultation for enhanced medical diagnosis. In particular, the experiments were carried out and achieved more than 88% in efficiency classification. The use of PCA showed that it could improve the outcomes classifications of inhalation-exhalation signals when noise was present, but PCA did not improve the efficiency classification of noise free S1-S2 signals. The GMM-HMM performed better applying MFCC vectors in both cases, inhalation-exhalation and S1-S2 events. The clusters analysis with Quartile vectors identifies more clusters in inhalation-exhalation signals than in S1-S2 signals. These classification outcomes drive the implementation of events modeling techniques, and also the implementation of automated auscultation with a simple laptop, which could be found in most medical offices, even in rural areas, and utilized for assisted diagnosis and documentation.