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Multi-source signal processing in phonocardiography: comparison among signal selection and signal enhancement techniques*

机译:语气动脉造影中的多源信号处理:信号选择和信号增强技术之间的比较 *

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Phonocardiography (PCG) is a promising tool for the diagnosis and follow-up of cardiovascular diseases. To date it is available only in clinical settings, because it relies on an experienced examiner for the positioning of the electronic stethoscope. Making it possible for an unexperienced user to obtain high quality PCG signals would allow for developing instruments suitable for homecare purposes. In this work, we test the usage of three standardly positioned microphone probes. The aim is to compare two different approaches for enhancing the PCG signal quality, namely a) selecting the single source with the highest Signal-to-Noise Ratio (SNR) and b) combining the three sources through array signal processing techniques. Both approaches were tested on a sample population counting 24 healthy subjects. We found that the two approaches above give statistically different results (two-tailed paired t-test, p = 0.037) in terms of SNR of the enhanced signal. Specifically, selecting the single source with highest SNR gives, on average, the best results. Moreover, this approach is also associated with the lowest computational cost. Finally, for every subject of our sample population, we obtained SNR values higher than 12.5 dB on the enhanced signal, which we consider as sufficient for the application of heart sound segmentation and classification algorithms. We believe that this methodology allows for obtaining PCG signals of sufficient quality for the analysis of heart sounds, thus opening to the applicability of PCG in a homecare context.
机译:PhoneCarcography(PCG)是诊断和随访心血管疾病的有希望的工具。迄今为止,它仅适用于临床环境,因为它依赖于经验丰富的审查员进行电子听诊器的定位。使得未经验的用户可以获得高质量的PCG信号,可以允许开发适合于主页的仪器。在这项工作中,我们测试了三种标准定位的麦克风探头的使用。目的是比较用于增强PCG信号质量的两种不同方法,即A)选择具有最高信噪比(SNR)和B)通过阵列信号处理技术的最高信噪比(SNR)和B)。两种方法都在计数24个健康受试者的样本群体上进行测试。我们发现,在增强信号的SNR方面,上述两种方法在统计上提供统计学上不同的结果(双尾配对T检验,P = 0.037)。具体而言,选择具有最高SNR的单个源,平均赋予最佳效果。此外,这种方法也与最低计算成本相关联。最后,对于我们样本群体的每个主题,我们在增强的信号上获得了高于12.5 dB的SNR值,我们认为这是为了应用心音分割和分类算法的应用。我们认为该方法允许获得足够质量的PCG信号,以便分析心脏声音,从而打开PCG在HomeCare背景下的适用性。

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