基于机器学习的电阻抗心容积监测信号质量评估
Machine Learning-based Signal Quality Assessment for Cardiac Volume Monitoring in Electrical Impedance Tomography
摘要：
由于胸部电阻抗断层成像的最新进展，可以通过监视与每搏输出量和心输出量相关联的心脏体积信号来无创地和连续地实时估计患者的血液动力学功能。然而，在临床应用中，心脏体积信号通常质量低，这主要是因为患者在临床干预期间的故意移动或不可避免的运动。本研究旨在开发一种信号质量指数方法，评估运动伪影对瞬态心脏容积信号的影响。在每个心动周期上进行评估，以利用心脏容积变化的周期性和规律性。使用同步心电图系统确定时间间期。我们采用不同的机器学习方法，可以分为判别模型和流形学习方法。机器学习的使用可能适用于我们的实时监控应用，这些应用需要快速推理和自动化以及高精度。在临床环境中，所提出的方法可以用于提供即时警告，使得临床医生可以最小化关于患者状况的混淆，减少临床资源利用，并且提高监测系统的置信水平。使用实际EIT数据进行的大量实验验证了通过机器学习实时准确、自动评估因运动伪影而退化的心脏容积信号的能力。最佳模型的准确度为0.95，阳性和阴性预测值为0.96和0.86，灵敏度为0.98，特异性为0.77，AUC为0.96。
英文摘要：
Owing to recent advances in thoracic electrical impedance tomography, a patient's hemodynamic function can be noninvasively and continuously estimated in real-time by surveilling a cardiac volume signal associated with stroke volume and cardiac output. In clinical applications, however, a cardiac volume signal is often of low quality, mainly because of the patient's deliberate movements or inevitable motions during clinical interventions. This study aims to develop a signal quality indexing method that assesses the influence of motion artifacts on transient cardiac volume signals. The assessment is performed on each cardiac cycle to take advantage of the periodicity and regularity in cardiac volume changes. Time intervals are identified using the synchronized electrocardiography system. We apply divergent machine-learning methods, which can be sorted into discriminative-model and manifold-learning approaches. The use of machine-learning could be suitable for our real-time monitoring application that requires fast inference and automation as well as high accuracy. In the clinical environment, the proposed method can be utilized to provide immediate warnings so that clinicians can minimize confusion regarding patients' conditions, reduce clinical resource utilization, and improve the confidence level of the monitoring system. Numerous experiments using actual EIT data validate the capability of cardiac volume signals degraded by motion artifacts to be accurately and automatically assessed in real-time by machine learning. The best model achieved an accuracy of 0.95, positive and negative predictive values of 0.96 and 0.86, sensitivity of 0.98, specificity of 0.77, and AUC of 0.96.
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