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.

瓣膜性心脏病（VHD）是心血管疾病的主要原因之一，在世界范围内死亡率很高。VHD的早期诊断可以防止心脏病的发展，并可以提供最佳的药物治疗。尽管当前的黄金标准具有识别VHD的能力，但它们仍缺乏所需的准确性，因此，有些情况会被误诊。因此，本文进行了一项研究，以研究深度学习模型通过心电图（PCG）记录识别VHD的效率。PCG心音是从代表正常心音的开放式访问数据集以及四个主要VHD中获得的；即主动脉瓣狭窄（AS），二尖瓣狭窄（MS），二尖瓣关闭不全（MR）和二尖瓣脱垂（MVP）。共1个 000名患者参与了研究，每节课有200条记录。最初将所有记录修整为具有9,600个样本，以确保它们覆盖至少1个心动周期。此外，通过应用最大重叠离散小波变换（MODWT）平滑算法对它们进行了预处理，并且$ž$-分数标准化。设计神经网络体系结构以减少文献中经常发现的复杂性，它由基于双向长期短期记忆（BiLSTM）的卷积神经网络（CNN）和递归神经网络（RNN）组合而成。使用CNN-BiLSTM网络以及CNN和BiLSTM，分别按照10倍的k倍交叉验证方案对模型进行了训练和测试。使用CNN-BiLSTM网络可获得最高的性能，总Cohen卡伯值，准确度，灵敏度和特异性为97.87$％，$ 99.32$％，$ 98.30$％，$ 和99.58$％，$分别。此外，模型的曲线下平均面积（AUC）为0.998。此外，该模型的性能在PhysioNet / Computing in Cardiology 2016挑战数据集上进行了评估，整体准确性达到87.31$％$AUC为0.900。这项研究为在临床环境下在VHD识别中实施深度学习模型铺平了道路，以帮助临床医生做出决策并防止许多病例因心脏异常而发展。