BACKGROUND Heart failure (HF) is a type of cardiovascular disease caused by abnormal cardiac structure and function. Early screening of HF has important implication for treatment in a timely manner. Heart sound (HS) conveys relevant information related to HF; this study is therefore based on the analysis of HS signals. The objective is to develop an efficient tool to identify subjects of normal, HF with preserved ejection fraction and HF with reduced ejection fraction automatically. METHODS We proposed a novel HF screening framework based on gated recurrent unit (GRU) model in this study. The logistic regression-based hidden semi-Markov model was adopted to segment HS frames. Normalized frames were taken as the input of the proposed model which can automatically learn the deep features and complete the HF screening without de-nosing and hand-crafted feature extraction. RESULTS To evaluate the performance of proposed model, three methods are used for comparison. The results show that the GRU model gives a satisfactory performance with average accuracy of 98.82%, which is better than other comparison models. CONCLUSION The proposed GRU model can learn features from HS directly, which means it can be independent of expert knowledge. In addition, the good performance demonstrates the effectiveness of HS analysis for HF early screening.

中文翻译：

---

基于门控循环单元的心音分析，用于心力衰竭筛查。

背景技术心力衰竭（HF）是由异常的心脏结构和功能引起的一种心血管疾病。HF的早期筛查对及时治疗具有重要意义。心音（HS）传达与HF相关的信息；因此，本研究基于对HS信号的分析。目的是开发一种自动识别正常受试者，射血分数保留的HF和射血分数降低的HF的有效工具。方法在本研究中，我们提出了一种基于门控递归单元（GRU）模型的新型HF筛查框架。采用基于逻辑回归的隐藏半马尔可夫模型对HS帧进行分割。归一化帧被作为所提出模型的输入，该模型可以自动学习深度特征并完成HF筛选，而无需消除噪声和手工提取特征。结果为了评估所提出模型的性能，使用了三种方法进行比较。结果表明，GRU模型具有令人满意的性能，平均精度为98.82％，优于其他比较模型。结论提出的GRU模型可以直接从HS学习特征，这意味着它可以独立于专家知识。此外，良好的性能证明了HS分析对HF早期筛查的有效性。结果表明，GRU模型具有令人满意的性能，平均精度为98.82％，优于其他比较模型。结论提出的GRU模型可以直接从HS学习特征，这意味着它可以独立于专家知识。此外，良好的性能证明了HS分析对HF早期筛查的有效性。结果表明，GRU模型具有令人满意的性能，平均精度为98.82％，优于其他比较模型。结论提出的GRU模型可以直接从HS学习特征，这意味着它可以独立于专家知识。此外，良好的性能证明了HS分析对HF早期筛查的有效性。