Deep learning models for segmenting phonocardiogram signals: a comparative study

Hiam Alquran

doi:10.1371/journal.pone.0320297

2025-04-14

DOI：10.1371/journal.pone.0320297

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摘要

En 中文

心脏听诊需要身体表面发生的机械振动，这些振动携带一定范围的声音频率。这些声音是由不同心脏结构在促进血液循环时的运动和搏动产生的。随后，这些声音被识别为心音图（PCG）。在本研究中，应用了深度学习模型，即门控循环神经网络（GRU）、双向门控循环神经网络（Bidirectional-GRU）和双向长短期记忆网络（BILSTM），分别对PCG信号中的四个特定区域进行分割，即S1（ lub声）、收缩期区域、S2（ dub声）和舒张期区域。这些模型应用于三个知名数据集：PhysioNet/Computing in Cardiology Challenge 2016、麻省理工学院（MITHSDB）和CirCor DigiScope心音图。PCG信号经过一系列预处理步骤，包括数字滤波和经验模态分解，之后应用深度学习算法以实现最高的分割精度。值得注意的是，所提出的方法在PhysioNet数据集上达到了97.2%的准确率，在MITHSDB数据集上达到了96.98%的准确率。值得注意的是，本文首次对CirCor DigiScope数据集的分割过程进行了研究，准确率达到92.5%。本研究比较了上述数据集中各种深度学习模型的性能，证明了其在医疗环境中的效率、准确性和可靠性作为软件工具的优越性。

期刊

PLoS One

IF:

2.6

论文数:

2.6W

被引数:

81.6W

机构

king hussein med ctr

学者数:

论文数: 3

被引数: 0

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