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

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Oliveira, J (Author) Other; Mantadelis, T (Author) FCUP; Renna, F (Author) FCUP; Gomes, P (Author) Other; Coimbra, M (Author) FCUP

Conference proceedings International

Title: 2017 IEEE INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING SYSTEMS (SIPS)

Pages: 1-6

IEEE International Workshop on Signal Processing Systems (SiPS)

Lorient, FRANCE, OCT 03-05, 2017

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 6 Citations

Other information

Authenticus ID: P-00N-9PP

DOI: 10.1109/sips.2017.8110004

Abstract (EN): 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 approximate to 83% up to approximate to 90% of positive predictability per sample.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6