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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >Empirical Wavelet Transform Based Lung Sound Removal from Phonocardiogram Signal for Heart Sound Segmentation
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Empirical Wavelet Transform Based Lung Sound Removal from Phonocardiogram Signal for Heart Sound Segmentation

机译：基于经验小波变换的心音分割从心电图信号中去除肺音

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摘要

Automatic removal of lung sounds from a phonocardiogram (PCG) signal is most essential for accurately detecting and recognizing the fundamental heart sounds such as the first heart sound (S1) and second heart sound (S2). In this paper, we propose an automated lung sound removal method using the empirical wavelet transform (EWT). The proposed method consists of three major stages: the EWT based signal decomposition; the frequency based mode selection; and the signal reconstruction. The proposed method is evaluated by synthetically adding the different lung sounds available in Littmann lung sound library with the real-time recorded PCG signals from 20 volunteers. The quality of the reconstructed signals is assessed by using both objective quality assessment metrics and subjective quality test such as mean opinion score (MOS). For the performance comparison, two lung sound removal methods have been implemented based on the ensemble empirical mode decomposition (EEMD) and frequency selective filtering techniques. The objective and subjective evaluation results and the heart sound segmentation results demonstrate that the EWT based lung sound removal method outperforms the other methods. The proposed method based heart sound segmentation scheme achieves an average sensitivity (Se) of 100%, positive predictivity (Pp) of 99.22%, and overall accuracy (OA) of 99.22%.
机译：从心电图（PCG）信号中自动清除肺部声音对于准确检测和识别基本心音（例如第一心音（S1）和第二心音（S2））至关重要。在本文中，我们提出了一种使用经验小波变换（EWT）的自动肺部声音去除方法。所提出的方法包括三个主要阶段：基于EWT的信号分解;以及基于EWT的信号分解。基于频率的模式选择;以及信号重建。通过将Littmann肺部声音库中可用的不同肺部声音与来自20位志愿者的实时记录的PCG信号进行合成相结合，对提出的方法进行评估。通过使用客观质量评估指标和主观质量测试（例如，平均意见得分（MOS））来评估重构信号的质量。为了进行性能比较，基于整体经验模式分解（EEMD）和频率选择性滤波技术，已实现了两种肺部声音去除方法。客观和主观的评估结果以及心音分割结果表明，基于EWT的肺音去除方法优于其他方法。所提出的基于方法的心音分割方案可实现100％的平均灵敏度（Se），正预测性（P p ）为99.22％，整体准确度（OA）为99.22％。

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