Hidden Markov model-based classification of heart valve disease with PCA for dimension reduction

In this study, a biomedical system to classify heart sound signals obtained with a stethoscope, has been proposed. For this purpose, data from healthy subjects and those with cardiac valve disease (pulmonary stenosis (PS) or mitral stenosis (MS)) have been used to develop a diagnostic model. Feature extraction from heart sound signals has been performed. These features represent heart sound signals in the frequency domain by Discrete Fourier Transform (DFT). The obtained features have been reduced by a dimension reduction technique called principal component analysis (PCA). A discrete hidden Markov model (DHMM) has been used for classification. This proposed PCA-DHMM-based approach has been applied on two data sets (a private and a public data set). Experimental classification results show that the dimension reduction process performed by PCA has improved the classification of heart sound signals.