Validation of a new impedance cardiography analysis algorithm for clinical classification of stress states. (12th February 2022) - Sheikh, Shafa‐at Ali; Gurel, Nil Z.; Gupta, Shishir; Chukwu, Ikenna V.; Levantsevych, Oleksiy; Alkhalaf, Mhmtjamil; Soudan, Majd; Abdulbaki, Rami; Haffar, Ammer; Clifford, Gari D.; Inan, Omer T.; Shah, Amit J.
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. Abstract : Although impedance cardiography is a promising method for detecting mortality in the general population, hypertension management, and stress assessment in psychophysiology research, its effectiveness can be severely limited by artifacts caused by speaking and movement. We present a validation study for a three‐stage ensemble‐average noise removal method during speaking tasks and show that this method more accurately identifies noisy signals resulting in better classification of stress states than the conventional method. The proposed method can also be applied to many other settings in which motion may lead to misclassification of cardiac physiology based on impedance cardiogram signal.