Gated recurrent unit-based heart sound analysis for heart failure screening
Gated recurrent unit-based heart sound analysis for heart failure screening
复制标题
基于门控循环单元的心音分析用于心力衰竭筛查
DOI：
10.1186/s12938-020-0747-x
复制
发表时间：
2020-01-13
影响因子：
3.9
通讯作者：
Guo, Xingming
中科院分区：
文献类型：
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作者：
Gao, Shan;Zheng, Yineng;Guo, Xingming
心力衰竭（Heartfailure，HF）是由心脏结构和功能异常引起的心血管疾病. HF的早期筛查对于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HS信号的分析。目的是开发一种有效的工具，以确定主题正常，HF保留射血分数和HF减少射血fractionatomic.MethodsWe提出了一种新的HF筛选框架的基础上门控复发单位（GRU）模型在这项研究中。采用基于Logistic回归的隐半马尔可夫模型对HS帧进行分割。该模型以归一化帧为输入，可以自动学习深层特征，无需去噪和手工特征提取即可完成高频筛选。实验结果表明，GRU模型具有良好的性能，平均准确率为98.82%，优于其他比较模型。结论GRU模型可以直接从HS中学习特征，不依赖于专家知识。此外，良好的性能证明了HS分析用于HF早期筛查的有效性。
BackgroundHeart 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.MethodsWe 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.ResultsTo 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.ConclusionThe 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.