Classification System for Heart Sounds Based on Random Forests

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In the last century, cardiovascular illnesses are the first death cause in developed countries. For this reason, many efforts have been made in order to develop sophisticated techniques for the early diagnoses of cardiac disorders. The Phonocardiogram (PCG) signals contain very useful information about the condition of the heart. By analyzing these signals, early detection and diagnosis of heart diseases can be done. It is also very useful in the case of infants, where ECG recording and other techniques are difficult to implement. In this study, a classification method is proposed to classify normal and abnormal heart sound signals using random forests algorithm. The proposed framework was applied to a database of 100 heart sound signals which collected from the web site, firstly all the signals were processed using the wavelet technique to eliminate the noise from the signal, features were extracted from the enhanced signals and the most significant features was selected using the RFs. Finally the random forests classifier was used to perform the classification process. The system achieved 93.24% accuracy in distinguishing between normal and abnormal heart sound signals.

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