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首页> 外文期刊>Elektronika ir Elektrotechnika >Automatic Detection of Heartbeats in Heart Sound Signals Using Deep Convolutional Neural Networks
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Automatic Detection of Heartbeats in Heart Sound Signals Using Deep Convolutional Neural Networks

机译：使用深卷积神经网络自动检测心声信号中的心跳

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摘要

The analysis of non-stationary signals commonly includes the signal segmentation process, dividing such signals into smaller time series, which are considered stationary and thus easier to process. Most commonly, the methods for signal segmentation utilize complex filtering, transformation and feature extraction techniques together with various kinds of classifiers, which especially in the field of biomedical signals, do not perform very well and are generally prone to poor performance when dealing with signals obtained in highly variable environments. In order to address these problems, we designed a new method for the segmentation of heart sound signals using deep convolutional neural networks, which works in a straightforward automatic manner and does not require any complex pre-processing. The proposed method was tested on a set of heartbeat sound clips, collected by non-experts with mobile devices in highly variable environments with excessive background noise. The obtained results show that the proposed method outperforms other methods, which are taking advantage of using domain knowledge for the analysis of the signals. Based on the encouraging experimental results, we believe that the proposed method can be considered as a solid basis for the further development of the automatic segmentation of highly variable signals using deep neural networks.
机译：非静止信号的分析通常包括信号分割过程，将这些信号划分为较小的时间序列，其被认为是静止的，从而更容易处理。最常见的是，使用复杂的滤波，转换和特征提取技术的信号分割方法以及各种分类器，特别是在生物医学信号领域中，不执行良好，并且通常在处理所获得的信号时容易出现性能不佳在高度可变的环境中。为了解决这些问题，我们设计了一种利用深卷积神经网络进行心声信号分割的新方法，该方法以简单的自动方式工作，并且不需要任何复杂的预处理。所提出的方法在一组心跳声剪辑上测试，由非专家收集，其中具有在具有过度背景噪声的高度可变环境中的移动设备。所获得的结果表明，该方法优于其他方法，这是利用域名知识来分析信号。基于令人鼓舞的实验结果，我们认为，该方法可以被认为是使用深神经网络进一步发展高变量信号的自动分割的坚实基础。

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