Autoregressive modeling of diastolic heart sounds

Diastolic heart sound segments were modelled by autoregressive (AR) methods, including recursive least-square lattice (RLSL) and gradient-lattice predictors. An application of the Akaike criterion demonstrated that between 5 and 22 AR coefficients are required to describe a diastolic segment completely. The reflection coefficients, prediction coefficients, zeros of the polynomial of the inverse filter, and the AR spectrum were determined over a number (N=20-30) of diastolic segments. Preliminary results indicate that the AR spectrum and the zeros of the inverse filter polynomial can be used to distinguish between normal patients and those with coronary artery disease.<
>