IEEE Trans Biomed Circuits Syst. 2019 Dec;13(6):1494-1505. doi: 10.1109/TBCAS.2019.2948303. Epub 2019 Oct 18.
Point-of-care screening for hemodialysis vascular access dysfunction requires tools that are objective and efficient. Listening for bruits during physical exam is a subjective examination which can detect stenosis (vascular narrowing) when properly performed. Phonoangiograms (PAGs)-mathematical analysis of bruits-increases the objectivity and sensitivity and permits quantification of stenosis location and degree of stenosis (DOS). This work describes a flexible and body-conformal multi-channel sensor and associated signal processing methods for automated DOS characterization of vascular access. The sensor used an array of thin-film PVDF microphones integrated on polyimide to record bruits at multiple sites along a vascular access. Nonlinear signal processing was used to extract spectral features, and cardiac cycle segmentation was used to improve sensitivity. PAG signal processing algorithms to detect stenosis location and severity are also presented. Experimental results using microphone arrays on a vascular access phantom demonstrated that stenotic lesions were detected within 1 cm of the actual location and graded to three levels (mild, moderate, or severe). Additional PAG features were also used to define a simple binary classifier aimed at patients with failing vascular accesses. The classifier achieved 90% accuracy, 92% specificity, and 91% sensitivity at detecting stenosis greater than 50%. These results suggest that point-of-care screening using microphone arrays can identify at-risk patients using automated signal analysis.
即时检测血液透析血管通路功能障碍需要客观且高效的工具。在体格检查中听诊杂音是一种主观检查,如果操作得当,可以检测到狭窄(血管变窄)。声图(PAG)-对杂音的数学分析-提高了客观性和敏感性,并允许量化狭窄位置和狭窄程度(DOS)。这项工作描述了一种灵活且与身体贴合的多通道传感器以及用于自动血管通路 DOS 特征描述的相关信号处理方法。该传感器使用集成在聚酰亚胺上的薄膜 PVDF 麦克风阵列在血管通路的多个部位记录杂音。使用非线性信号处理来提取频谱特征,并使用心脏周期分割来提高灵敏度。还提出了用于检测狭窄位置和严重程度的 PAG 信号处理算法。在血管通路模型上使用麦克风阵列的实验结果表明,狭窄病变在实际位置 1cm 内被检测到,并分为三个级别(轻度、中度或重度)。还使用了其他 PAG 特征来定义一个简单的二进制分类器,旨在用于检测功能不良的血管通路患者。该分类器在检测大于 50%的狭窄时达到了 90%的准确性、92%的特异性和 91%的敏感性。这些结果表明,使用麦克风阵列进行即时检测可以使用自动信号分析来识别高危患者。