To what extent can we shorten HRV analysis in wearable sensing? A case study on mental stress detection

Mental stress is one of the first causes of cognitive dysfunctions, cardiovascular disorders and depression. In addition, it reduces performances, on the work place and in daily life. The diffusion of wearable sensors (embedded in smart-watches, phones, etc.) has opened up the potential to assess mental stress detection through ultra-short term Heart Rate Variability (HRV) analysis (i.e., less than 5 min). Although informative analyses of features coming from short HRV (i.e., 5 min) have already been performed, the reliability of ultra-short HRV remains unclear. This study aims to tackle this gap by departing from a systematic review of the existing literature and investigating, in healthy subjects, the associations between acute mental stress and short/ultra-short term HRV features in time, frequency, and non-linear domains. Building on these findings, three experiments were carried out to empirically assess the usefulness of HRV for mental stress detection using ultra-short term analysis and wearable devices. Experiment 1 detected mental stress in a real life situation by exploring to which extent HRV excerpts can be shortened without losing their ability to detect mental stress. This allowed us to advance a method to explore to what extent ultra-short HRV features can be considered as good surrogates of 5 min HRV features. Experiment 2 and 3 sought to develop automatic classifiers to detect mental stress through 2 min HRV excerpts, by using a Stroop Color Word Test (CWT) and a highly paced video game, which are two common laboratory-based stressors. Results from experiment 1 demonstrated that 7 ultra-short HRV features can be considered as good surrogates of short HRV features in detecting mental stress in real life. By leveraging these 7 features, experiment 2 and 3 offered an automatic classifier detecting mental stress with ultra-short features (2min), achieving sensitivity, specificity and accuracy rate above 60%. Mental stress is one of the first causes of cognitive dysfunctions, cardiovascular disorders and depression. In addition, it reduces performances, on the work place and in daily life. The diffusion of wearable sensors (embedded in smart-watches, phones, etc.) has opened up the potential to assess mental stress detection through ultra-short term Heart Rate Variability (HRV) analysis (i.e., less than 5 min). Although informative analyses of features coming from short HRV (i.e., 5 min) have already been performed, the reliability of ultra-short HRV remains unclear. This study aims to tackle this gap by departing from a systematic review of the existing literature and investigating, in healthy subjects, the associations between acute mental stress and short/ultra-short term HRV features in time, frequency, and non-linear domains. Building on these findings, three experiments were carried out to empirically assess the usefulness of HRV for mental stress detection using ultra-short term analysis and wearable devices. Experiment 1 detected mental stress in a real life situation by exploring to which extent HRV excerpts can be shortened without losing their ability to detect mental stress. This allowed us to advance a method to explore to what extent ultra-short HRV features can be considered as good surrogates of 5 min HRV features. Experiment 2 and 3 sought to develop automatic classifiers to detect mental stress through 2 min HRV excerpts, by using a Stroop Color Word Test (CWT) and a highly paced video game, which are two common laboratory-based stressors. Results from experiment 1 demonstrated that 7 ultra-short HRV features can be considered as good surrogates of short HRV features in detecting mental stress in real life. By leveraging these 7 features, experiment 2 and 3 offered an automatic classifier detecting mental stress with ultra-short features (2min), achieving sensitivity, specificity and accuracy rate above 60%.