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A benchmark of heart sound classification systems based on sparse decompositions

Abstract : Background: Nowadays, cardiovascular diseases (CVD) remain the main cause of death worldwide. A heart sound signal or phonocardiogram (PCG) is the most simple, economical and non-invasive tool to detect CVDs. Advances in technology and signal processing allow the design of computer-aided systems for heart illnesses detection from PCG signals. Purpose: The paper proposes a pipeline and benchmark for binary heart sounds classification. The features extraction architecture is focused on the use of Matching Pursuit time-frequency decomposition using Gabor dictionaries and the Linear Predictive Coding method of a residual. We compare seven classifiers with two different approaches: feature averaging and cycle averaging. Methods: We test our proposal on the PhysioNet/CinC challenge 2016 database, which comprises a wide variety of heart sounds recorded from patients with normal and different pathological heart conditions. We conduct a 10-fold stratified cross-validation method to evaluate the performance of different classification algorithms. The feature sets were also tested when using an oversampling method for balancing. Results: The benchmark identified systems showing a satisfying performance in terms of accuracy, sensitivity, and Matthews correlation coefficient. Results can be improved when using feature averaging and an oversampling strategy.
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Contributor : Nancy Bertin <>
Submitted on : Monday, November 26, 2018 - 2:24:13 PM
Last modification on : Saturday, July 11, 2020 - 3:15:02 AM
Long-term archiving on: : Wednesday, February 27, 2019 - 2:06:42 PM


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  • HAL Id : hal-01935058, version 1


Roilhi Ibarra-Hernández, Nancy Bertin, Miguel Alonso-Arévalo, Hugo Guillén-Ramírez. A benchmark of heart sound classification systems based on sparse decompositions. SIPAIM 2018 - 14th International Symposium on Medical Information Processing and Analysis, Oct 2018, Mazatlán, Mexico. pp.1-14. ⟨hal-01935058⟩



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