Heart sound classification based on scaled spectrogram and tensor decomposition
Heart sound classification based on scaled spectrogram and tensor decomposition
复制标题
基于缩放频谱图和张量分解的心音分类
DOI：
10.1016/j.eswa.2017.05.014
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发表时间：
2017-10
影响因子：
8.5
通讯作者：
Deng Shiwen
中科院分区：
文献类型：
--
作者：
Zhang Wen-Jie;Han Ji-Qing;Deng Shiwen
心音信号分析是心脏病初步诊断的一种有效、简便的方法。然而，心音的自动分类仍然是一个具有挑战性的问题，主要体现在心音的分割以及从相应的分割结果中提取特征。为了提取更有鉴别力的心音特征用于心音分类，提出了一种基于尺度谱图和张量分解的方法。在所提出的方法中，首先将检测到的心动周期的频谱图缩放到固定大小。然后，对缩放后的谱图进行降维处理，以提取最具鉴别力的特征。在降维过程中，利用张量分解方法提取了包含心音信号重要生理和病理信息的标度谱图的内在结构。因此，所提取的特征更具鉴别力。最后，利用支持向量机（SVM）完成分类任务。此外，该方法在PASCAL分类心音挑战和2016 PhysioNet挑战提供的三个公共数据集上进行了评估。结果表明，该方法具有较强的竞争力。
Heart sound signal analysis is an effective and convenient method for the preliminary diagnosis of heart disease. However, automatic heart sound classification is still a challenging problem which mainly reflected in heart sound segmentation and feature extraction from the corresponding segmentation results. In order to extract more discriminative features for heart sound classification, a scaled spectrogram and tensor decomposition based method was proposed in this study. In the proposed method, the spectrograms of the detected heart cycles are first scaled to a fixed size. Then a dimension reduction process of the scaled spectrograms is performed to extract the most discriminative features. During the dimension reduction process, the intrinsic structure of the scaled spectrograms, which contains important physiological and pathological information of the heart sound signals, is extracted using tensor decomposition method. As a result, the extracted features are more discriminative. Finally, the classification task is completed by support vector machine (SVM). Moreover, the proposed method is evaluated on three public datasets offered by the PASCAL classifying heart sounds challenge and 2016 PhysioNet challenge. The results show that the proposed method is competitive.
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影响因子：
3.8
作者：
Quiceno-Manrique, A. F.;Godino-Llorente, J. I.;Castellanos-Dominguez, G.
通讯作者：
Castellanos-Dominguez, G.
影响因子：
6.1
作者：
Shuping Sun;Zhongwei Jiang;Haibin Wang;Yu Fang
通讯作者：
Shuping Sun;Zhongwei Jiang;Haibin Wang;Yu Fang
DOI：
10.1145/2628194.2628197
发表时间：
2014-07
期刊：
Proceedings of the 18th International Database Engineering & Applications Symposium
影响因子：
--
作者：
S. Oliveira;E. F. Gomes;A. Jorge
通讯作者：
S. Oliveira;E. F. Gomes;A. Jorge
DOI：
10.22489/cinc.2016.327-210
发表时间：
2016-09
期刊：
2016 Computing in Cardiology Conference (CinC)
影响因子：
--
作者：
Hsuan-Lin Her;Hung-Wen Chiu
通讯作者：
Hsuan-Lin Her;Hung-Wen Chiu
影响因子：
--
作者：
ELHARRAR, V;SURAWICZ, B
通讯作者：
SURAWICZ, B