Convolutional and recurrent neural networks for the detection of valvular heart diseases in phonocardiogram recordings. (March 2021)

• Record Type: Journal Article
• Authors: Alkhodari, Mohanad; Fraiwan, Luay
• Is Part Of: Computer methods and programs in biomedicine. Volume 200(2021)
• Publication Date: 2021-03
• Subjects: Cardiovascular disease (CVD) -- Valvular heart diseases (CHD) -- Phonocardiography (PCG) -- Heart sounds recordings -- Deep learning -- Convolutional neural networks (CNN) -- Bi-directional long short-term memory (BiLSTM) -- Training and classification; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.105940
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Proposing a methodology for the classification of heart sounds based on convolutional neural network and bi-directional long short-term memory (CNN-BiLSTM) model that is able of extracting combined spatial and temporal features from the sound recordings. Classifying with high performance the 5 major types of valvular heart diseases (VHD) such as the aortic stenosis (AS), mitral stenosis (MS), mitral regurgitation (MR), and mitral valve pro-lapse (MVP), using a raw heart phonocardiography (PCG) recordings. Achieving improved levels of performance compared to other studies found in literature with an overall Cohens kappa, accuracy, sensitivity, and specificity of 97.87%, 99.32%, 98.30%, and 99.50%. The study paves the way towards implementing simple, yet effective, deep learning models in VHD identification under clinical settings to assist clinicians in critical decision making. Abstract: Valvular heart diseases (VHD) are one of the major causes of cardiovascular diseases that are having high mortality rates worldwide. The early diagnosis of VHD prevents the development of cardiac diseases and allows for optimum medication. Despite of the ability of current gold standards in identifying VHD, they still lack the required accuracy and thus, several cases go misdiagnosed. In this vein, a study is conducted herein to investigate the efficiency of deep learning models in identifying VHD through phonocardiography (PCG) recordings. PCG heart sounds were obtained from an open-access data-set representing normal heart sounds along with four major VHD; namely aortic stenosis (AS), mitral stenosis (MS), mitral regurgitation (MR), and mitral valve prolapse (MVP). A total of 1, 000 patients were involved in the study with 200 recordings for each class. All recordings were initially trimmed to have 9, 600 samples ensuring their coverage of at least 1 cardiac cycle. In addition, they were pre-processed by applying maximal overlap discrete wavelet transform (MODWT) smoothing algorithm and z -score normalization. The neural network architecture was designed to reduce the complexity often found in literature and consisted of a combination of convolutional neural networks (CNN) and recurrent neural networks (RNN) based on Bi-directional long short-term memory (BiLSTM). The model was trained and tested following a k-fold cross-validation scheme of 10-folds utilizing the CNN-BiLSTM network as well as the CNN and BiLSTM, individually. The highest performance was achieved using the CNN-BiLSTM network with an overall Cohen's kappa, accuracy, sensitivity, and specificity of 97.87 %, 99.32 %, 98.30 %, and 99.58 %, respectively. In addition, the model had an average area under the curve (AUC) of 0.998. Furthermore, the performance of the model was assessed on the PhysioNet/Computing in Cardiology 2016 challenge data-set and reached an overall accuracy of 87.31 % with an AUC of 0.900. This study paves the way towards implementing deep learning models in VHD identification under clinical settings to assist clinicians in decision making and prevent many cases from cardiac abnormalities development.