Heart Sounds Classification Using Images from Wavelet Transformation

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Nogueira, DM (Author) Other; Zarmehri, MN (Author) Other; Ferreira, CA (Author) Other; Jorge, AM (Author) FCUP; antunes, l (Author) FCUP

Conference proceedings International

Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Pages: 311-322

19th EPIA Conference on Artificial Intelligence, EPIA 2019

3 September 2019 through 6 September 2019

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 6 Citations

Other information

Authenticus ID: P-00R-21C

DOI: 10.1007/978-3-030-30241-2_27

Abstract (EN): Cardiovascular disease is the leading cause of death around the world and its early detection is a key to improving long-term health outcomes. To detect possible heart anomalies at an early stage, an automatic method enabling cardiac health low-cost screening for the general population would be highly valuable. By analyzing the phonocardiogram (PCG) signals, it is possible to perform cardiac diagnosis and find possible anomalies at an early-term. Accordingly, the development of intelligent and automated analysis tools of the PCG is very relevant. In this work, the PCG signals are studied with the main objective of determining whether a PCG signal corresponds to a ¿normal¿ or ¿abnormal¿ physiological state. The main contribution of this work is the evidence provided that time domain features can be combined with features extracted from a wavelet transformation of PCG signals to improve automatic cardiac disease classification. We empirically demonstrate that, from a pool of alternatives, the best classification results are achieved when both time and wavelet features are used by a Support Vector Machine with a linear kernel. Our approach has obtained better results than the ones reported by the challenge participants which use large amounts of data and high computational power. © Springer Nature Switzerland AG 2019.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12