A Data-Driven Feature Extraction Method for Enhanced Phonocardiogram Segmentation

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

Authors

Renna, F (Author) FCUP; Oliveira, J (Author) Other; Coimbra, M (Author) FCUP

Conference proceedings International

Title: 2017 COMPUTING IN CARDIOLOGY (CINC)

Pages: 1-4

44th Computing in Cardiology Conference (CinC)

Rennes, FRANCE, SEP 24-27, 2017

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 1 Citation

Other information

Authenticus ID: P-00N-W4E

DOI: 10.22489/cinc.2017.183-281

Abstract (EN): In 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 F-1 score of 92.6% in detecting S-1 and S-2 sounds.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4