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首页> 外文期刊>Biomedical and Health Informatics, IEEE Journal of >Deep Convolutional Neural Networks for Heart Sound Segmentation
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Deep Convolutional Neural Networks for Heart Sound Segmentation

机译：用于心音分割的深度卷积神经网络

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This paper studies the use of deep convolutional neural networks to segment heart sounds into their main components. The proposed methods are based on the adoption of a deep convolutional neural network architecture, which is inspired by similar approaches used for image segmentation. Different temporal modeling schemes are applied to the output of the proposed neural network, which induce the output state sequence to be consistent with the natural sequence of states within a heart sound signal (S1, systole, S2, diastole). In particular, convolutional neural networks are used in conjunction with underlying hidden Markov models and hidden semi-Markov models to infer emission distributions. The proposed approaches are tested on heart sound signals from the publicly available PhysioNet dataset, and they are shown to outperform current state-of-the-art segmentation methods by achieving an average sensitivity of 93.9 and an average positive predictive value of 94 in detecting S1 and S2 sounds.
机译：本文研究了深度卷积神经网络的使用，将心音分为主要成分。所提出的方法基于采用深度卷积神经网络体系结构，该体系结构受到用于图像分割的类似方法的启发。将不同的时间建模方案应用于所提出的神经网络的输出，这会导致输出状态序列与心音信号（S1，收缩压，S2，舒张期）中的自然状态序列一致。尤其是，将卷积神经网络与基础的隐马尔可夫模型和隐半马尔可夫模型结合使用以推断排放分布。在来自公开的PhysioNet数据集的心音信号上测试了所提出的方法，通过检测S1的平均灵敏度达到93.9和94的平均阳性预测值，它们表现出优于当前最新的分割方法和S2声音。

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