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A fusion framework based on multi-domain features and deep learning features of phonocardiogram for coronary artery disease detection.
Computers in Biology and Medicine ( IF 7.0 ) Pub Date : 2020-03-30 , DOI: 10.1016/j.compbiomed.2020.103733 Han Li 1 , Xinpei Wang 1 , Changchun Liu 1 , Qiang Zeng 2 , Yansong Zheng 2 , Xi Chu 3 , Lianke Yao 1 , Jikuo Wang 1 , Yu Jiao 1 , Chandan Karmakar 4
Computers in Biology and Medicine ( IF 7.0 ) Pub Date : 2020-03-30 , DOI: 10.1016/j.compbiomed.2020.103733 Han Li 1 , Xinpei Wang 1 , Changchun Liu 1 , Qiang Zeng 2 , Yansong Zheng 2 , Xi Chu 3 , Lianke Yao 1 , Jikuo Wang 1 , Yu Jiao 1 , Chandan Karmakar 4
Affiliation
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Phonocardiogram (PCG) signals reflect the mechanical activity of the heart. Previous studies have reported that PCG signals contain heart murmurs caused by coronary artery disease (CAD). However, the murmurs caused by CAD are very weak and rarely heard by the human ear. In this paper, a novel feature fusion framework is proposed to provide a comprehensive basis for CAD diagnosis. A dataset containing PCG signals of 175 subjects was collected and used. A total of 110 features were extracted from multiple domains, and then reduced and selected. Images obtained by Mel-frequency cepstral coefficients were used as the input for the convolutional neural network for feature learning. Then, the selected features and the deep learning features were fused and fed into a multilayer perceptron for classification. The proposed feature fusion method achieved better classification performance than multi-domain features or deep learning features alone, with accuracy, sensitivity, and specificity of 90.43%, 93.67%, and 83.36%, respectively. A comparison with existing studies demonstrated that the proposed method was a promising noninvasive screening tool for CAD under general medical conditions.
中文翻译：
基于心电图的多域特征和深度学习特征的融合框架，用于检测冠状动脉疾病。
心音图（PCG）信号反映了心脏的机械活动。先前的研究已报道PCG信号包含由冠状动脉疾病（CAD）引起的心脏杂音。但是，由CAD引起的杂音非常微弱，人耳几乎听不到。本文提出了一种新颖的特征融合框架，为CAD诊断提供了全面的基础。收集并使用了包含175个受试者的PCG信号的数据集。从多个域中提取了总共110个特征，然后进行了缩减和选择。通过梅尔频率倒谱系数获得的图像用作用于特征学习的卷积神经网络的输入。然后，将选定的特征和深度学习特征融合并馈入多层感知器中进行分类。提出的特征融合方法比单独的多域特征或深度学习特征实现了更好的分类性能，其准确度，灵敏度和特异性分别为90.43％，93.67％和83.36％。与现有研究的比较表明，该方法在一般医学条件下是一种很有前途的CAD无创筛查工具。
更新日期：2020-04-20
中文翻译：
基于心电图的多域特征和深度学习特征的融合框架，用于检测冠状动脉疾病。
心音图（PCG）信号反映了心脏的机械活动。先前的研究已报道PCG信号包含由冠状动脉疾病（CAD）引起的心脏杂音。但是，由CAD引起的杂音非常微弱，人耳几乎听不到。本文提出了一种新颖的特征融合框架，为CAD诊断提供了全面的基础。收集并使用了包含175个受试者的PCG信号的数据集。从多个域中提取了总共110个特征，然后进行了缩减和选择。通过梅尔频率倒谱系数获得的图像用作用于特征学习的卷积神经网络的输入。然后，将选定的特征和深度学习特征融合并馈入多层感知器中进行分类。提出的特征融合方法比单独的多域特征或深度学习特征实现了更好的分类性能，其准确度，灵敏度和特异性分别为90.43％，93.67％和83.36％。与现有研究的比较表明，该方法在一般医学条件下是一种很有前途的CAD无创筛查工具。
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