A machine learning approach to detect aortic valve dysfunction through phase portrait feature extraction

The paper reports a novel method of early detection of heart valve dysfunction, drawing phase portrait and power spectral features of normal (NM) and aortic stenosis (AS) murmur signals. The 40 signals of NM and AS are subjected to spectral, fractal, and nonlinear time series analyses. Using machine learning techniques, the signals are classified and predicted based on phase portrait and power spectral features.The appearance of multiple...