基于tsallis熵的肺声特征提取

Achmad Rizal, Risanuri Hidayat, H. A. Nugroho
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引用次数: 16

摘要

肺裂音是由呼吸道异常引起的。肺裂音是一种间断的、持续时间短的肺音,出现在吸气期、呼气期或两者同时出现。研究人员使用了各种方法来自动检测裂纹声,例如使用熵测量。Tsallis熵是熵的一种度量,它具有非扩张性。萨利斯熵常用于测量快速变化的信号。爆裂声同时具有这两种特性,因此Tsallis熵可以作为肺部爆裂声的特征提取技术。测试结果表明,使用非延展性阶为q = 2、3和4的Tsallis熵可以产生最高的精度。采用MLP和3倍交叉验证,准确率为95.35%,灵敏度为90.48%,特异性为100%。该方法的优点是产生的特征数量少,计算简单。使用数据类和未来研究所需的更大数据的数量进行测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pulmonary crackle feature extraction using tsallis entropy for automatic lung sound classification
pulmonary crackle sound is produced by an abnormality in the respiratory tract. Pulmonary crackle sound is one of lung sound that is discontinuous, short duration and appears on the inspiratory phase, expiratory phase or both. Various methods are used by researchers to detect crackle sound automatically, for example using entropy measurement. Tsallis entropy is a measure of the entropy that has nonextensivity property. Tsallis entropy is often used to measure rapidly changing signals. Crackle sound has both of properties, so hopefully, Tsallis entropy can be utilized as feature extraction techniques for pulmonary crackle sound. The test results showed the use of Tsallis entropy with nonextensivity order of q = 2, 3, and 4 produce the highest accuracy. Using MLP and 3fold crossvalidation, an accuracy of 95.35%, Sensitivity of 90.48%, and 100% Specificity are achieved. The advantage of this method is the fewer number of features produced and simple computation. Tests using data classes and the number of larger data required in future studies.
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