Neurofuzzy modelling of lung sounds
Neurofuzzy modelling of lung sounds
复制标题
肺音的神经模糊建模
DOI：
--
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Costas Hilas
中科院分区：
文献类型：
--
作者：
George Kandilogiannakis;P. Mastorocostas;Dimitrios Varsamis;Costas Hilas
本文提出了一种基于计算智能的滤波器，用于实时分离肺泡音中的偶发间断肺音。该滤波器使用两个动态模糊神经网络来执行分离肺音的任务，这些肺音是从患有肺部病理的患者中获得的。该网络由模拟退火动态弹性传播算法训练，所得滤波器应用于三个主要类别的肺音。为了突出所提出的分离方案的学习特性和性能，进行了广泛的实验分析，与其他滤波器的比较。
In this paper a computational intelligence-based filter for real-time separation the adventitious discontinuous lung sounds from the vesicular sounds is proposed. The filter uses two Dynamic Fuzzy Neural Networks to perform the task of separation of the lung sounds, obtained from patients with pulmonary pathology. The networks are trained by the Simulated Annealing Dynamic Resilient Propagation algorithm and the resulting filter is applied to three major classes of lung sounds. In order to highlight the learning characteristics and the performance of the proposed separation scheme, extensive experimental analysis is conducted, where a comparison with other filters is given.