Post-Traumatic Stress Disorder (PTSD) is a psychiatric condition resulting from threatening or horrifying events. We hypothesized that circadian rhythm changes, measured by a wrist-worn research watch are predictive of post-trauma outcomes. APPROACH: 1618 post-trauma patients were enrolled after admission to emergency departments (ED). Three standardized questionnaires were administered at week eight to measure post-trauma outcomes related to PTSD, sleep disturbance, and pain interference with daily life. Pulse activity and movement data were captured from a research watch for eight weeks. Standard and novel movement and cardiovascular metrics that reflect circadian rhythms were derived using this data. These features were used to train different classifiers to predict the three outcomes derived from week-eight surveys. Clinical surveys administered at ED were also used as features in the baseline models. RESULTS: The highest cross-validated performance of research watch-based features was achieved for classifying participants with pain interference by a logistic regression model, with an area under the receiver operating characteristic curve (AUC) of 0.70. The ED survey-based model achieved an AUC of 0.77, and the fusion of research watch and ED survey metrics improved the AUC to 0.79. SIGNIFICANCE: This work represents the first attempt to predict and classify post-trauma symptoms from passive wearable data using machine learning approaches that leverage the circadian desynchrony in a potential PTSD population.

In this paper, we investigate the potential of generic physiological features of stress resilience in predicting air traffic control (ATC) candidates' performance in a highly-stressful low-fidelity ATC simulator scenario. Stress resilience is highlighted as an important occupational factor that influences the performance and well-being of air traffic control officers (ATCO). Poor stress management, besides the lack of skills, can be a direct cause of poor performance under stress, both in the selection process of ATCOs and later in the workplace. 40 ATC candidates, within the final stages of their selection process, underwent a stimulation paradigm for elicitation and assessment of various generic task-unrelated physiological features, related to resting heart rate variability (HRV) and respiratory sinus arrhythmia (RSA), acoustic startle response (ASR) and the physiological allostatic response, which are all recognized as relevant psychophysiological markers of stress resilience. The multimodal approach included analysis of electrocardiography, electromyography, electrodermal activity and respiration. We make advances in computational methodology for assessment of physiological features of stress resilience, and investigate the predictive power of the obtained feature space in a binary classification problem: prediction of high- vs. low-performance on the developed ATC simulator. Our novel approach yields a relatively high 78.16% classification accuracy. These results are discussed in the context of prior work, while considering study limitations and proposing directions for future work.

This paper presents an extensive statistical analysis of the acoustic startle response of two vocal parameters: fundamental frequency (F0) and root-mean-square energy (E), as well as of the orbicularis oculi (eyeblink) surface electromyography (sEMG). An experiment was conducted in which fourteen participants were exposed to acoustic startle stimuli of varying parameters, i.e., intensity level, duration, rise time, and spectral type, during periods of sustained phonation. Voice recordings of the phonations were taken alongside several physiological signals, of which only the sEMG was analyzed in this paper. Response features (peak value, peak time, latency, rise time, fall time, and duration) were extracted on F0, E and sEMG data, and statistical analysis was conducted using linear mixed effects models to show the response behavior with the varying stimuli. The results for vocal F0 and E data were congruent with sEMG data and earlier work in the field. The results demonstrated that vocal analysis can be used as a feasible alternative to the sEMG eyeblink analysis of acoustic startle responses.