RESUMO
Phonocardiogram (PCG) signal of the mitral valve prolapse (MVP) patients is characterized by transient audio events which include a systolic click (SC) followed by a murmur of varying intensity. Physicians detect these auscultation clues in regular auscultation before ordering expensive echocardio-graphy test. But auscultation is often error prone and even physicians with considerable experience might end up missing these clues. Therefore developing machine learning techniques to help clinicians is the need of the hour. A segmentation technique using Fourier synchrosqueezed transform (FSST) features with a long short term memory (LSTM) network is proposed in this study. An accuracy of 99.8% on MVP dataset demonstrates the potential of the proposed method in clinical diagnosis.
