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Owing to recent advances in thoracic electrical impedance tomography, apatient's hemodynamic function can be noninvasively and continuously estimatedin real-time by surveilling a cardiac volume signal associated with strokevolume and cardiac output. In clinical applications, however, a cardiac volumesignal is often of low quality, mainly because of the patient's deliberatemovements or inevitable motions during clinical interventions. This study aimsto develop a signal quality indexing method that assesses the influence ofmotion artifacts on transient cardiac volume signals. The assessment isperformed on each cardiac cycle to take advantage of the periodicity andregularity in cardiac volume changes. Time intervals are identified using thesynchronized electrocardiography system. We apply divergent machine-learningmethods, which can be sorted into discriminative-model and manifold-learningapproaches. The use of machine-learning could be suitable for our real-timemonitoring application that requires fast inference and automation as well ashigh accuracy. In the clinical environment, the proposed method can be utilizedto provide immediate warnings so that clinicians can minimize confusionregarding patients' conditions, reduce clinical resource utilization, andimprove the confidence level of the monitoring system. Numerous experimentsusing actual EIT data validate the capability of cardiac volume signalsdegraded by motion artifacts to be accurately and automatically assessed inreal-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.

Machine Learning-based Signal Quality Assessment for Cardiac Volume Monitoring in Electrical Impedance Tomography