Validation of a new impedance cardiography analysis algorithm for clinical classification of stress states

• Type of Material: Article
• Authors: Shafa-at Ali Sheikh, Emory University; Nil Z Gurel, Neurocardiology Research Center of Excellence and Cardiac Arrhythmia Center, David Geffen School of Medicine at UCLA, Los Angeles, USA; Shishir Gupta, Emory University; Ikenna V Chukwu, Emory University; Oleksiy Levantsevych, Emory University; Mhmtjamil Alkhalaf, Emory University; Majd Soudan, Emory University; Rami Abdulbaki, Emory University; Ammer Haffar, Emory University; Gari Clifford, Emory University; Omer T Inan, School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA; Amit Shah, Emory University
• Language: English
• Date: 2022-02-12
• Publisher: WILEY
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 1999-2024 John Wiley & Sons
• Final Published Version (URL): http://dx.doi.org/10.1111/psyp.14013
• Title of Journal or Parent Work: PSYCHOPHYSIOLOGY
• Volume: 59
• Issue: 7
• Start Page: e14013
• End Page: e14013
• Grant/Funding Information: National Institutes of Health (Grant # NIH K23HL127251, R01HL136205, R01HL125246, R01HL130619, and R03HL146879), the National Science Foundation Award 1636933, and Emory University
• Keywords: PREEJECTION; NERVOUS-SYSTEM; DISORDER; three-stage ensemble average noise removal method; Physiology; Psychology, Experimental; PERIOD; pre-ejection period; CARDIOVASCULAR REACTIVITY; Social Sciences; Neurosciences; emotional tasks; impedance cardiography; Psychology, Biological; transcutaneous vagal nerve stimulation; Science & Technology; Life Sciences & Biomedicine; Psychology; Neurosciences & Neurology; stress state classification
• Research Categories: Engineering, Electronics and Electrical; Engineering, Biomedical; Health Sciences, Medicine and Surgery; Computer Science; Health Sciences, Epidemiology

Abstract

Pre-ejection period (PEP) is an index of sympathetic nervous system activity that can be computed from electrocardiogram (ECG) and impedance cardiogram (ICG) signals, but sensitive to speech/motion artifact. We sought to validate an ICG noise removal method, three-stage ensemble-average algorithm (TEA), in data acquired from a clinical trial comparing active versus sham non-invasive vagal nerve stimulation (tcVNS) after standardized speech stress. We first compared TEA's performance versus the standard conventional ensemble-average algorithm (CEA) approach to classify noisy ICG segments. We then analyzed ECG and ICG data to measure PEP and compared group-level differences in stress states with each approach. We evaluated 45 individuals, of whom 23 had post-traumatic stress disorder (PTSD). We found that the TEA approach identified artifact-corrupted beats with intraclass correlation coefficient > 0.99 compared to expert adjudication. TEA also resulted in higher group-level differences in PEP between stress states than CEA. PEP values were lower in the speech stress (vs. baseline rest) group using both techniques, but the differences were greater using TEA (12.1 ms) than CEA (8.0 ms). PEP differences in groups divided by PTSD status and tcVNS (active vs. sham) were also greater when using the TEA versus CEA method, although the magnitude of the differences was lower. In conclusion, TEA helps to accurately identify noisy ICG beats during speaking stress, and this increased accuracy improves sensitivity to group-level differences in stress states compared to CEA, suggesting greater clinical utility.