Heart Sound Diagnose System with BFCC, MFCC, and Backpropagation Neural Network

Tarumanagara International Conference on the Applications of Technology and Engineering

工业技术（总论）

Lubis, Chairisni^1 ; Gondawijaya, Felicia^1

Informatics Engineering Department, Faculty of Information Technology Universitas Tarumanagara, Indonesia^1

关键词: Back propagation neural networks;    Cepstral coefficients;    Diagnose system;    Feature extraction methods;    Heart sounds;    Learning methods;    Mel frequency cepstral co-efficient;    Testing data;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/508/1/012119/pdf
DOI  :  10.1088/1757-899X/508/1/012119

学科分类：工业工程学

来源: IOP

【 摘 要 】

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%.