PHONOCARDIOGRAPHYBASED MITRAL VALVE PROLAPSE DETECTION USING AN ARTIFICIAL NEURAL NETWORK

Mitral valve prolapse (MVP) is the most common valve anomaly and the most frequent cause of isolated mitral insuffi-ciency. MVP has a mostly benign course and prognosis in child-hood; however, complications, such as severe mitral regurgita-tion, infectious endocarditis, pulmonary embolism, arrhythmia and sudden death, occur more often in elderly people, demon-strating the need for prompt diagnostics and prevention. Due to its frequent occurrence, failures in diagnosing MVP and the clini-cal importance of early MVP detection, the aim of this study was to develop an original, non-invasive and easily applicable diag-nostic method for MVP detection in children and adolescents by using an artifi cial neural network (ANN). Cardiac sounds were recorded by auscultation using electronic stethoscope in 48 children with MVP, 49 healthy children and 38 children wit