Abstract:
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.
