On modifying the temporal modeling of HSMMs for pediatric heart sound segmentation

Heart sounds are difficult to interpret because a) they are composed by several different sounds, all contained in very tight time windows; b) they vary from physiognomy even if the show similar characteristics; c) human ears are not naturally trained to recognize heart sounds. Computer assisted decision systems may help but they require robust signal processing algorithms. In this paper, we use a real life dataset in order to compare the performance of a hidden Markov model and several hidden semi Markov models that used the Poisson, Gaussian, Gamma distributions, as well as a non-parametric probability mass function to model the sojourn time. Using a subject dependent approach, a model that uses the Poisson distribution as an approximation for the sojourn time is shown to outperform all other models. This model was able to recreate the “true” state sequence with a positive predictability per state of 96%. Finally, we used a conditional distribution in order to compute the confidence of our classifications. By using the proposed confidence metric, we were able to identify wrong classifications and boost our system (in average) from an ≈ 83% up to ≈90% of positive predictability per sample.