Assessment of multichannel lung sounds parameterization for two-class classification in interstitial lung disease patients
Assessment of multichannel lung sounds parameterization for two-class classification in interstitial lung disease patients
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DOI：
10.1016/j.compbiomed.2011.04.009
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发表时间：
2011-07-01
影响因子：
7.7
通讯作者：
Aljama-Corrales, T.
中科院分区：
文献类型：
--
作者：
Charleston-Villalobos, S.;Martinez-Hernandez, G.;Aljama-Corrales, T.
这项工作涉及评估不同参数化技术的肺音（LS）获得整个后胸表面正常与异常的IS分类。在功率谱密度（PSD）的基础上，利用协方差矩阵的特征值以及单变量自回归模型（UAR）和多变量自回归模型（MAR）构造特征向量作为监督神经网络（SNN）的输入。结果表明，使用新数据的UAR建模对多通道IS参数化的有效性，对健康受试者和患者的分类准确率分别为75%和93%。(C) 2011 Elsevier Ltd.版权所有。
This work deals with the assessment of different parameterization techniques for lung sounds (LS) acquired on the whole posterior thoracic surface for normal versus abnormal IS classification. Besides the conventional technique of power spectral density (PSD), the eigenvalues of the covariance matrix and both the univariate autoregressive (UAR) and the multivariate autoregressive models (MAR) were applied for constructing feature vectors as input to a supervised neural network (SNN). The results showed the effectiveness of the UAR modeling for multichannel IS parameterization, using new data, with classification accuracy of 75% and 93% for healthy subjects and patients, respectively. (C) 2011 Elsevier Ltd. All rights reserved.