The alarmingly high mortality rate and increasing global prevalence of cardiovascular diseases signify the crucial need for early detection schemes. Phonocardiogram (PCG) signals have been historically applied in this domain owing to its simplicity and cost-effectiveness. In this paper, we propose CardioXNet, a novel lightweight end-to-end CRNN architecture for automatic detection of five classes of cardiac auscultation namely normal, aortic stenosis, mitral stenosis, mitral regurgitation and mitral valve prolapse using raw PCG signal. The process has been automated by the involvement of two learning phases. Three parallel CNN pathways have been implemented in the representation learning phase to learn the coarse and fine-grained features from the PCG and to explore the salient features from variable receptive fields involving 2D-CNN based squeeze-expansion. Thus, in the representation learning phase, the network extracts efficient time-invariant features and converges with great rapidity. In the sequential residual learning phase, with the bidirectional-LSTMs and the skip connection, the network can proficiently extract temporal features without performing any feature extraction on the signal. The obtained results demonstrate that the proposed end-to-end architecture yields outstanding performance in all the evaluation metrics compared to the previous state-of-the-art methods with up to 99.60% accuracy, 99.56% precision, 99.52% recall and 99.68% F1- score on an average while being computationally comparable. This model outperforms the previous works using the same dataset by a considerable margin. The high accuracy metrics on both primary and secondary dataset combined with a significantly low number of parameters and end-to-end prediction approach makes the proposed network suitable for point of care CVD screening in low resource setups using memory constraint mobile devices.

翻译：99.68 心血管疾病死亡率高得惊人,而且全球发病率日益上升,这表明迫切需要早期检测计划。Pono心电图(PCG)信号由于简单和成本效益而历来应用于该领域。在本论文中,我们提议CardioXNet,这是一个全新的轻轻端端至端CRNN 结构,用于自动检测五类心脏培养,即正常的、心肺萎缩、肺萎缩、线性振动和肺动阀通过原始PCG信号而爆裂99.68。这一过程通过两个学习阶段的参与而自动化。在演示学习阶段,实施了三个平行CNN 路径。在演示阶段,学习了PCG的粗度和细细度特征,并探索了与2D-CN的挤压扩张有关的可变容字段的突出特征。因此,在演示阶段,网络提取了高效的时间变化特征,并且与极快的存储阶段一致。在连续的远程学习阶段,通过双向LSTM和中继连接,在演示阶段实施了三个平行的CNNM路径。网络可以提取时间定位特性,而没有进行前期的进度评估,而前期的缩缩缩缩缩缩缩缩缩缩缩缩数据,同时用前的缩缩缩缩缩算数据,同时进行前的缩缩缩缩缩缩缩算。