Heart Sound Classification using Residual Neural Network and Convolution Block Attention Module

Enoch Frimpong, Qin Zhiguang, Tenagyei Edwin Kwadwo, Patamia Agbeshi Rutherford, E. Baagyere, Regina Esi Turkson
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Abstract

Listening to the heart sound with digital or manual stethoscopes has become one of the practical ways to identify heart diseases in recent years. It's still difficult because of its manual approach and the fact that only experienced healthcare practitioners can use it to diagnose anomalies. The automatic extraction of heart sound features to aid in classification has been explored, however there is still potential for improvement. This paper proposes a residual neural network integrated with a convolutional block attention module (CBAM) for heart sound analysis, using generated Mel-spectrograms as input for our network. We tested our model using the Pascal Heart Sound Challenge dataset, and it performed favorably to other cutting-edge models.
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基于残差神经网络和卷积块注意模块的心音分类
近年来,用数字式或手动听诊器听心音已成为诊断心脏病的实用方法之一。这仍然很困难,因为它采用手动方法,而且只有经验丰富的医疗从业人员才能使用它来诊断异常。心音特征的自动提取以辅助分类已被探索,但仍有改进的潜力。本文提出了一种结合卷积块注意模块(CBAM)的残差神经网络,将生成的mel谱图作为网络的输入,用于心音分析。我们使用Pascal心音挑战数据集测试了我们的模型,它比其他尖端模型表现得更好。
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