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

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.

Impedance cardiogram based exploration of cardiac mechanisms in post‐traumatic stress disorder during trauma recall

“…The subsequent two filtering stages employed cross‐correlation techniques to identify and discard − dZ / dt beats that were corrupted by artifacts and displayed a significantly different morphology compared to the average − dZ / dt beat templates. Ultimately, the clean − dZ / dt beats, along with their corresponding Z ( t ) and ECG beats, were accumulated and subjected to ensemble averaging, resulting in the generation of the final synchronized EA beats (Sheikh, Shah, Levantsevych, et al., 2020; Sheikh et al., 2022a).…”

Data‐driven approach for automatic detection of aortic valve opening: B point detection from impedance cardiogram

“…Synchronized ECG and ICG signals were recorded by trained researchers using impedance and electrocardiography equipment (BIOPAC Systems Inc., Goleta, CA). BIOPAC module settings, and electrode configuration used for recording ECG and ICG signals for both studies can be found in our prior work (Sheikh, Shah, Levantsevych, et al, 2020; Sheikh et al, 2022) For some subjects (three in study A, and four in study B), no/noisy signal was observed due to electrode disconnect/improper connections during some tasks. These specific data were discarded from the analysis.…”