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End-to-end heart sound segmentation using deep convolutional recurrent network
Complex & Intelligent Systems ( IF 5.0 ) Pub Date : 2021-03-29 , DOI: 10.1007/s40747-021-00325-w
Yao Chen , Yanan Sun , Jiancheng Lv , Bijue Jia , Xiaoming Huang

Heart sound segmentation (HSS) aims to detect the four stages (first sound, systole, second heart sound and diastole) from a heart cycle in a phonocardiogram (PCG), which is an essential step in automatic auscultation analysis. Traditional HSS methods need to manually extract the features before dealing with HSS tasks. These artificial features highly rely on extraction algorithms, which often result in poor performance due to the different operating environments. In addition, the high-dimension and frequency characteristics of audio also challenge the traditional methods in effectively addressing HSS tasks. This paper presents a novel end-to-end method based on convolutional long short-term memory (CLSTM), which directly uses audio recording as input to address HSS tasks. Particularly, the convolutional layers are designed to extract the meaningful features and perform the downsampling, and the LSTM layers are developed to conduct the sequence recognition. Both components collectively improve the robustness and adaptability in processing the HSS tasks. Furthermore, the proposed CLSTM algorithm is easily extended to other complex heart sound annotation tasks, as it does not need to extract the characteristics of corresponding tasks in advance. In addition, the proposed algorithm can also be regarded as a powerful feature extraction tool, which can be integrated into the existing models for HSS. Experimental results on real-world PCG datasets, through comparisons to peer competitors, demonstrate the outstanding performance of the proposed algorithm.



中文翻译:

使用深度卷积递归网络的端到端心音分割

心音分割(HSS)旨在从心动图(PCG)的心动周期中检测出四个阶段(第一声音,心脏收缩,第二心脏声音和心脏舒张),这是自动听诊分析中必不可少的步骤。传统的HSS方法需要在处理HSS任务之前手动提取特征。这些人造特征高度依赖提取算法,由于不同的操作环境,提取算法通常会导致性能不佳。此外,音频的高维度和频率特性也对有效解决HSS任务的传统方法提出了挑战。本文提出了一种基于卷积长短期记忆(CLSTM)的新颖的端到端方法,该方法直接使用音频记录作为输入来解决HSS任务。特别,卷积层旨在提取有意义的特征并执行下采样,而LSTM层则用于进行序列识别。这两个组件共同提高了处理HSS任务的鲁棒性和适应性。此外,由于不需要预先提取相应任务的特征,因此所提出的CLSTM算法很容易扩展到其他复杂的心音注释任务。另外,该算法也可以看作是功能强大的特征提取工具,可以集成到现有的HSS模型中。通过与同行竞争对手进行比较,在真实PCG数据集上的实验结果证明了该算法的出色性能。并开发了LSTM层来进行序列识别。这两个组件共同提高了处理HSS任务的鲁棒性和适应性。此外,由于不需要预先提取相应任务的特征,因此所提出的CLSTM算法很容易扩展到其他复杂的心音注释任务。另外,该算法也可以看作是功能强大的特征提取工具,可以集成到现有的HSS模型中。通过与同行竞争对手进行比较,在真实PCG数据集上的实验结果证明了该算法的出色性能。并开发了LSTM层来进行序列识别。这两个组件共同提高了处理HSS任务的鲁棒性和适应性。此外,由于不需要预先提取相应任务的特征,因此所提出的CLSTM算法很容易扩展到其他复杂的心音注释任务。另外,该算法也可以看作是功能强大的特征提取工具,可以集成到现有的HSS模型中。通过与同行竞争对手进行比较,在真实PCG数据集上的实验结果证明了该算法的出色性能。提出的CLSTM算法很容易扩展到其他复杂的心音注释任务,因为它不需要提前提取相应任务的特征。另外,该算法也可以看作是功能强大的特征提取工具,可以集成到现有的HSS模型中。通过与同行竞争对手进行比较,在真实PCG数据集上的实验结果证明了该算法的出色性能。提出的CLSTM算法很容易扩展到其他复杂的心音注释任务,因为它不需要提前提取相应任务的特征。另外,该算法也可以看作是功能强大的特征提取工具,可以集成到现有的HSS模型中。通过与同行竞争对手进行比较,在真实PCG数据集上的实验结果证明了该算法的出色性能。

更新日期:2021-03-29
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