Robust heart sound activity detection in noisy environments

A novel and robust method for heart sound activity detection (HSAD) is presented. In this method, a new discriminative feature is developed based on the lag-1 autocorrelation coefficient and feature smoothing is introduced to identify the endpoints of low-level heart murmurs. Several experiments on a large scale phonocardiogram database show that the method significantly outperforms the other HSAD methods under varying levels of heart sounds and different types of noise. For a signal-to-noise ratio of 5 dB, the method achieves a sensitivity of 99.56% and a positive predictivity of 99.41% and has a smallest average time error value of 4.26 ms.