Show simple item record

A Data-Driven Feature Extraction Method for Enhanced Phonocardiogram Segmentation

dc.creatorRenna, Francesco
dc.creatorOliveira, J
dc.creatorCoimbra, MT
dc.date.accessioned2017-09-01
dc.date.accessioned2018-11-24T23:20:43Z
dc.date.available2018-05-03T12:47:57Z
dc.date.available2018-11-24T23:20:43Z
dc.identifierhttps://www.repository.cam.ac.uk/handle/1810/275504
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/123456789/3637
dc.description.abstractIn this work, we present a method to extract features from heart sound signals in order to enhance segmentation performance. The approach is data-driven, since the way features are extracted from the recorded signals is adapted to the data itself. The proposed method is based on the extraction of delay vectors, which are modeled with Gaussian mixture model priors, and an information-theoretic dimensionality reduction step which aims to maximize discrimination between delay vectors in different segments of the heart sound signal. We test our approach with heart sounds from the publicly available PhysioNet dataset showing an average F1 score of 92.6% in detecting S1 and S2 sounds.
dc.languageen
dc.titleA Data-Driven Feature Extraction Method for Enhanced Phonocardiogram Segmentation
dc.typeConference Object


Files in this item

FilesSizeFormatView
CinC_Renna (2).pdf197.3Kbapplication/pdfView/Open

This item appears in the following Collection(s)

Show simple item record