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Post‐traumatic stress disorder (PTSD) is an independent risk factor for incident heart failure, but the underlying cardiac mechanisms remained elusive. Impedance cardiography (ICG), especially when measured during stress, can help understand the underlying psychophysiological pathways linking PTSD with heart failure. We investigated the association between PTSD and ICG‐based contractility metrics (pre‐ejection period (PEP) and Heather index (HI)) using a controlled twin study design with a laboratory‐based traumatic reminder stressor. PTSD status was assessed using structured clinical interviews. We acquired synchronized electrocardiograms and ICG data while playing personalized‐trauma scripts. Using linear mixed‐effects models, we examined twins as individuals and within PTSD‐discordant pairs. We studied 137 male veterans (48 pairs, 41 unpaired singles) from Vietnam War Era with a mean (standard deviation) age of 68.5(2.5) years. HI during trauma stress was lower in the PTSD vs. non‐PTSD individuals (7.2 vs. 9.3 [ohm/s²],p = .003). PEP reactivity (trauma minus neutral) was also more negative in PTSD vs. non‐PTSD individuals (−7.4 vs. −2.0 [ms],p = .009). The HI and PEP associations with PTSD persisted for adjusted models during trauma and reactivity, respectively. For within‐pair analysis of eight PTSD‐discordant twin pairs (out of 48 pairs), PTSD was associated with lower HI in neutral, trauma, and reactivity, whereas no association was found between PTSD and PEP. PTSD was associated with reduced HI and PEP, especially with trauma recall stress. This combination of increased sympathetic activation and decreased cardiac contractility combined may be concerning for increased heart failure risk after recurrent trauma re‐experiencing in PTSD.

Pre‐ejection period (PEP), an indicator of sympathetic nervous system activity, is useful in psychophysiology and cardiovascular studies. Accurate PEP measurement is challenging and relies on robust identification of the timing of aortic valve opening, marked as the B point on impedance cardiogram (ICG) signals. The ICG sensitivity to noise and its waveform's morphological variability makes automated B point detection difficult, requiring inefficient and cumbersome expert visual annotation. In this article, we propose a machine learning‐based automated algorithm to detect the aortic valve opening for PEP measurement, which is robust against noise and ICG morphological variations. We analyzed over 60 hr of synchronized ECG and ICG records from 189 subjects. A total of 3657 averaged beats were formed using our recently developed ICG noise removal algorithm. Features such as the averaged ICG waveform, its first and second derivatives, as well as high‐level morphological and critical hemodynamic parameters were extracted and fed into the regression algorithms to estimate the B point location. The morphological features were extracted from our proposed “variable” physiologically valid search‐window related to diverse B point shapes. A subject‐wise nested cross‐validation procedure was performed for parameter tuning and model assessment. After examining multiple regression models, Adaboost was selected, which demonstrated superior performance and higher robustness to five state‐of‐the‐art algorithms that were evaluated in terms of low mean absolute error of 3.5 ms, low median absolute error of 0.0 ms, high correlation with experts' estimates (Pearson coefficient = 0.9), and low standard deviation of errors of 9.2 ms. For reproducibility, an open‐source toolbox is provided.

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.