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07 February 2025 |
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Article overview
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Machine Learning-based Signal Quality Assessment for Cardiac Volume Monitoring in Electrical Impedance Tomography | Chang Min Hyun
; Tae Jun Jang
; Jeongchan Nam
; Hyeuknam Kwon
; Kiwan Jeon
; Kyunghun Lee
; | Date: |
4 Jan 2023 | Abstract: | 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. | Source: | arXiv, 2301.01469 | Services: | Forum | Review | PDF | Favorites |
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