Screening for abnormal heart sounds and murmurs by implementing neural networks

Visagie, Claude (2007-03)

Thesis (MScEng (Mechanical and Mechatronic Engineering))--University of Stellenbosch, 2007.


This thesis is concerned with the testing of an “auscultation jacket” as a means of recording heart sounds and electrocardiography (ECG) data from patients. A classification system based on Neural Networks, that is able to discriminate between normal and abnormal heart sounds and murmurs, has also been developed . The classification system uses the recorded data as training and testing data. This classification system is proposed to serve as an aid to physicians in diagnosing patients with cardiac abnormalities. Seventeen normal participants and 14 participants that suffer from valve-related heart disease have been recorded with the jacket. The “auscultation jacket” shows great promise as a wearable health monitoring aid for application in rural areas and in the telemedicine industry. The Neural Network classification system is able to differentiate between normal and abnormal heart sounds with a sensitivity of 85.7% and a specificity of 94.1%.

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