Heart sounds classification using motif based segmentation
Heart sounds classification using motif based segmentation
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DOI：
10.1145/2628194.2628197
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发表时间：
2014-07
期刊：
影响因子：
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通讯作者：
S. Oliveira;E. F. Gomes;A. Jorge
中科院分区：
文献类型：
--
作者：
S. Oliveira;E. F. Gomes;A. Jorge
在本文中，我们描述了一种算法的心音分类（类正常，杂音和期外收缩）的基础上离散化的声音信号使用的SAX（符号聚合近似）表示。一般的策略是自动发现相关的最高频率基序，并将它们与音频信号中的心脏收缩（S1）和心脏舒张（S2）声音的出现相关联。该算法使用从医院临床试验中获得的音频信号集合生成的图案进行调整。对一组单独的未标记音频信号进行验证。结果表明，能够提高类正常和杂音的分类精度。
In this paper we describe an algorithm for heart sound classification (classes Normal, Murmur and Extrasystole) based on the discretization of sound signals using the SAX (Symbolic Aggregate Approximation) representation. The general strategy is to automatically discover relevant top frequent motifs and relate them with the occurrence of systolic (S1) and diastolic (S2) sounds in the audio signals. The algorithm was tuned using motifs generated from a collection of audio signals obtained from a clinical trial in a hospital. Validation was performed on a separate set of unlabeled audio signals. Results indicate ability to improve the precision of the classification of the classes Normal and Murmur.