Machine learning-based signal quality assessment for cardiac volume monitoring in electrical impedance tomography

• Published in: Machine learning: science and technology Vol. 4; no. 1; pp. 15034 - 15051
• Main Authors: Min Hyun, Chang, Jun Jang, Tae, Nam, Jeongchan, Kwon, Hyeuknam, Jeon, Kiwan, Lee, Kyounghun
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.03.2023
• Subjects: Cardiac output; cardiac volume monitoring; Confidence intervals; Electrical impedance; electrical impedance tomography; Electrocardiography; Hemodynamics; Machine learning; Model accuracy; Monitoring; Quality assessment; Real time; Resource utilization; Signal quality; signal quality assessment; Stroke volume; Time synchronization; Tomography

Abstract Owing to recent advances in thoracic electrical impedance tomography (EIT), a patient’s hemodynamic function can be noninvasively and continuously estimated in real-time by surveilling a cardiac volume signal (CVS) associated with stroke volume and cardiac output. In clinical applications,...