Journal of medical engineering & technology
Feb 18, 2019
Cardiac auscultation is one of the most conventional approaches for the initial assessment of heart disease, however the technique is highly user-dependent and with low repeatability. Several computational approaches based on the analysis of the phon...
IEEE journal of biomedical and health informatics
Jan 21, 2019
This paper studies the use of deep convolutional neural networks to segment heart sounds into their main components. The proposed methods are based on the adoption of a deep convolutional neural network architecture, which is inspired by similar appr...
OBJECTIVE: Deep classification networks have been one of the predominant methods for classifying heart sound recordings. To satisfy their demand for sample size, the most commonly used method for data augmentation is that which divides each heart sou...
This paper proposes a method using multidomain features and support vector machine (SVM) for classifying normal and abnormal heart sound recordings. The database was provided by the PhysioNet/CinC Challenge 2016. A total of 515 features are extracted...
This study concerns the task of automatic structural heart abnormality risk detection from digital phonocardiogram (PCG) signals aiming at pediatric heart disease screening applications. Recently, various systems based on convolutional neural network...
IEEE transactions on bio-medical engineering
Jun 1, 2018
OBJECTIVE: In this paper, we accurately detect the state-sequence first heart sound (S1)-systole-second heart sound (S2)-diastole, i.e., the positions of S1 and S2, in heart sound recordings. We propose an event detection approach without explicitly ...
OBJECTIVE: Automatic heart sound analysis has the potential to improve the diagnosis of valvular heart diseases in the primary care phase, as well as in countries where there is neither the expertise nor the equipment to perform echocardiograms. An a...
UNLABELLED: Intensive care unit patients are heavily monitored, and several clinically-relevant parameters are routinely extracted from high resolution signals.
OBJECTIVE: This paper builds upon work submitted as part of the 2016 PhysioNet/CinC Challenge, which used sparse coding as a feature extraction tool on audio PCG data for heart sound classification.
OBJECTIVE: This paper describes a method for automated discrimination of heart sounds recordings according to the Physionet Challenge 2016. The goal was to decide if the recording refers to normal or abnormal heart sounds or if it is not possible to ...
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