Real-time separation of discontinuous adventitious sounds from vesicular sounds using a fuzzy rule-based filter
Real-time separation of discontinuous adventitious sounds from vesicular sounds using a fuzzy rule-based filter
复制标题
使用基于模糊规则的滤波器实时分离不连续的外来声音与囊泡声音
DOI：
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发表时间：
1998
影响因子：
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通讯作者：
S. Panas
中科院分区：
文献类型：
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作者：
Y. Tolias;L. Hadjileontiadis;S. Panas
病理性不连续不连续音(DAS)和泡状音(VS)的分离对于肺音的分析具有重要意义，因为DAS与某些肺部病理有关。根据DAS的非平稳性，提出了一种从VS中分离出DAS诊断特征的自动化方法。该算法使用两个基于自适应网络的模糊推理系统来构成一个基于广义模糊规则的平稳-非平稳滤波器(GFST-NST)。模糊推理系统的训练过程涉及Hadjileontiadis和Panas(1997)提出的基于小波变换的平稳-非平稳滤波器(WTST-NST)的输出。GFST-NST的基本思想最初由作者提出，引入了基于模糊规则的平稳-非平稳滤波器(FST-NST)(1997)，并用裂纹和VS分离进行了测试。本文的主要贡献是将FST-NST滤波器的结构改进为串行型模糊滤波器，与FST-NST滤波器的并行运算不同，它将预测的平稳信号(VS)送入非平稳信号的预测器(DAS)。将GFST-NST滤波器应用于从三个肺音数据库中选取的细粗声和尖叫声，揭示了DAS的相干结构，并将它们从VS中分离出来。通过定量和定性指标对GFST-NST过滤器的分离性能进行了评价，证明了GFST-NST过滤器相对于FST-NST过滤器的有效性和优越性。与WTST-NST过滤器相比，GFST-NST过滤器在准确性和客观性方面表现相似，但速度更快。因此，GFST-NST过滤器结合了WTST-NST过滤器的分离精度和FST-NST过滤器的实时实现，因此它可以作为集成的智能患者评估系统的一个模块轻松地用于临床医学。
The separation of pathological discontinuous adventitious sounds (DAS) from vesicular sounds (VS) is of great importance to the analysis of lung sounds since DAS are related to certain pulmonary pathologies. An automated way of revealing the diagnostic character of DAS, by isolating them from VS, based on their nonstationarity, is presented. The proposed algorithm uses two adaptive network-based fuzzy inference systems to compose a generalized fuzzy rule-based stationary-nonstationary filter (GFST-NST). The training procedure of the fuzzy inference systems involves the outputs of the wavelet transform-based stationary-nonstationary filter (WTST-NST), proposed by Hadjileontiadis and Panas (1997). The basic idea of the GFST-NST was initially proposed by the authors with the introduction of the fuzzy rule-based stationary-nonstationary filter (FST-NST) (1997), tested with the separation of crackles from VS. The main contribution of this paper is the modification of the structure of the FST-NST filter to a serial-type fuzzy filter that, unlike the parallel operation of the FST-NST filter, sends a predicted stationary signal (VS) into the predictor of the nonstationary (DAS). Applying the GFST-NST filter to fine-coarse crackles and squawks, selected from three lung sound databases, the coherent structure of DAS is revealed and they are separated from VS. The separation performance of the GFST-NST filter was evaluated through quantitative and qualitative indexes that proved its efficiency and superiority against the FST-NST filter. When compared to the WTST-NST filter, the GFST-NST filter performed similarly in accuracy and objectiveness, but in a faster way. Thus, the GFST-NST filter combines the separation accuracy of the WTST-NST filter with the real-time implementation of the FST-NST filter, so it can easily be used in clinical medicine as a module of an integrated intelligent patient evaluation system.