K. S. Yamuna, S. Thirunavukkarasu, B. Manjunatha, B. Karthikeyan
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引用次数: 0
摘要
肺音(LS)信号是识别肺部疾病的重要信息来源。心音(HS)是胸壁听诊过程中最常见的肺部杂音。这直接影响了肺音处理在诊断肺部疾病时的效率。在这项工作中,提出了自适应变异模式分解(AVMD)技术来去除肺部声音中的心音杂质。所提出的 AVMD 方法首先将嘈杂的肺音信号分解为称为变异模态函数(VMF)的带限模态集合。然后,根据频谱从 LS 中滤除 HS。我们收集了 95 名参与者的实时肺部声音数据,并使用统计指标对 VMD 技术的性能进行了评估。因此,所提出的拓扑结构具有更高的信噪比(29.6587dB)、最低的均方根(RMSE)(0.0102)、最低的归一化平均绝对误差(nMAE)(0.0336)以及最高的相关系数(CCF)(99.79%)。这些实验结果均优于最近提出的所有其他技术。
Elimination of heart sound from respiratory sound using adaptive variational mode decomposition for pulmonary diseases diagnosis
Lung sound (LS) signals are a vital source of information for the identification of pulmonary disorders. Heart sound (HS) is the most common contaminant of lung sounds during auscultation from the chest walls. This directly affects the efficiency of lung sound processing in diagnosing lung diseases. In this work, Adaptive Variational Mode Decomposition (AVMD) technique is proposed to remove heart sound contaminants from lung sounds. The proposed AVMD method initially breakdown the noisy lung sound signal into a collective of bandlimited modes called variational mode functions (VMF). Then, based on the frequency spectrum, the HS is filtered out from the LS. The real time lung sound data is collected from 95 participants and the performance of VMD technique is evaluated using the statistical metrics measures. Thus, the proposed topology exhibits Higher SNR (29.6587dB, lowest Root Mean Square (RMSE) of 0.0102, lowest normalized Mean Absolute Error (nMAE) of 0.0336, and highest percentage in correlation coefficient Factor (CCF) of 99.79% respectively. These experimental results are found to be superior and outperform all other recently proposed techniques.