Abstract
human heart produce 2 different sound which are lub-dub. Abnormal heart sound would produce additional sound such as whoosing, roaring, rumbling or turbulence fliud noise between the normal heart sounds. In general, diagnosing heart abnormalities is relying on doctors’ experience by hearing heart sound through stethoscope or using ECG. This research is using heart sound recording from The Pascal Classifying Heart Sound, Dataset B[1] as learning and testing data. Diagnosing heart sound with BFCC (Bark Frequency Cepstral Coefficients), MFCC (Mel Frequency Cepstral Coefficients), Modified BFCC, and Modified MFCC as feature extraction method and Backpropagation Neural Network as learning method. Heart sound recognition from The Pascal Classifying Heart Sound, Dataset Bwith BFCC is up to 79.167%, MFCC is up to 87.5%, Modified BFCC is up to 70.83%, and Modified MFCC is up to 95.83%.
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