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Introduction: Understanding the interplay between cardiovascular parameters, cognitive stress induced by increasing load, and mental well-being is vital for the development of integrated health strategies today. By monitoring physiological signals like electrocardiogram (ECG) and photoplethysmogram (PPG) in real time, researchers can discover how cognitive tasks influence both cardiovascular and mental health. Cardiac biomarkers resulting from cognitive strain act as indicators of autonomic nervous system function, potentially reflecting conditions related to heart and mental health, including depression and anxiety. The purpose of this study is to investigate how cognitive load affects ECG and PPG measurements and whether these can signal early cardiovascular changes during depression and anxiety disorders. Methods: Ninety participants aged 18 to 45 years, ranging from symptom-free individuals to those with diverse psychological conditions, were assessed using psychological questionnaires and anamnesis. ECG and PPG monitoring were conducted as volunteers engaged in a cognitive 1-back task consisting of two separate blocks, each with six progressively challenging levels. The participants' responses were analyzed to correlate physiological and psychological data with cognitive stressors and outcomes. Results: The study confirmed a notable interdependence between anxiety and depression, and cardiovascular responses. Task accuracy decreased with increased task difficulty. A strong relationship between PPG-measured heart rate and markers of depression and trait anxiety was observed. Increasing task difficulty corresponded to an increase in heart rate, linked with elevated levels of depression and trait anxiety. A strong relationship between ECG-measured heart rate and anxiety attacks was observed. Increasing task difficulty corresponded to an increase in heart rate, linked with elevated levels of anxiety attacks, although this association decreased under more challenging conditions. Discussion: The findings underscore the predictive importance of ECG and PPG heart rate parameters in mental health assessment, particularly depression and anxiety under cognitive stress induced by increasing load. We discuss mechanisms of sympathetic activation explaining these differences. Our research outcomes have implications for clinical assessments and wearable device algorithms for more precise, personalized mental health diagnostics.

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Nesfatin-1 and ghrelin, initially recognised as hormones involved in regulating energy, have emerged as crucial players with vital functions in various human body systems. In this study, we conducted a comparative assessment of nesfatin-1 and ghrelin responses in individuals experiencing metabolic stress due to diabetes, those with depressive diabetes characterised by both metabolic and mental stress, and healthy controls. We collected blood samples from a total of 90 participants, consisting of 30 people with type II diabetes mellitus (DM), 30 people with type II DM and major depressive disorders, and 30 healthy individuals. Diabetes was diagnosed based on glycated haemoglobin (HbA1c) levels, while depression was assessed using DSM-V criteria. Insulin resistance (HOMA-IR) was calculated, and serum ghrelin and nesfatin-1 levels were measured using ELISA kits. We observed statistically significant decreases in nesfatin-1 and ghrelin levels in the diabetic group (p < 0.0001). However, in the depressive diabetic group, nesfatin-1 levels increased significantly, while ghrelin levels decreased further. The nesfatin-1 to ghrelin ratio decreased in the diabetic group but increased significantly in the depressive diabetic group (p < 0.0001). Nesfatin-1 and ghrelin hormones exhibit parallel impacts in response to metabolic stress, but nesfatin-1 demonstrates contrasting actions compared to ghrelin when mental stress is added to metabolic stress. The findings of this study suggest that nesfatin-1 and ghrelin hormones may play active roles as protective, prognostic, and even etiological factors in various stress situations, particularly those involving mental stress, in addition to their known functions in regulating energy.

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Physiologically based stress detection systems have proven to be effective in identifying different stress conditions in the body to determine the source of stress and be able to counteract it. However, some stress conditions have not been widely studied, including thermal stress, cognitive stress, and combined (thermal-cognitive) stress conditions, which are frequently encountered in work or school environments. In order to develop systems to detect and differentiate these conditions, it is necessary to identify the physiological indicators that characterize each of them. The present research aims to identify which physiological indicators (heart rate, respiratory rate, galvanic skin response, and local temperature) could differentiate different stress conditions (no-stress, cognitive stress, thermal stress, and combined (thermal-cognitive) stress conditions). Thirty participants were exposed to cognitive, thermal, and combined stress sources while recording their physiological signals. The findings indicate that both mean heart rate and mean galvanic skin response identify moderate thermal and cognitive stress conditions as distinct from a no-stress condition, yet they do not differentiate between the two stress conditions. Additionally, heart rate uniquely identifies the cognitive-thermal stress condition, effectively distinguishing this combined stress condition from the singular stress conditions and the no-stress condition. Mean local temperature specifically signals thermal stress conditions, whereas mean respiratory rate accurately identifies cognitive stress conditions, with both indicators effectively separating these conditions from each other and from the no-stress condition. This is the first basis for differentiating thermal and cognitive stress conditions through physiological indicators.

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Multimodal emotion recognition techniques are increasingly essential for assessing mental states. Image-based methods, however, tend to focus predominantly on overt visual cues and often overlook subtler mental state changes. Psychophysiological research has demonstrated that heart rate (HR) and skin temperature are effective in detecting autonomic nervous system (ANS) activities, thereby revealing these subtle changes. However, traditional HR tools are generally more costly and less portable, while skin temperature analysis usually necessitates extensive manual processing. Advances in remote photoplethysmography (r-PPG) and automatic thermal region of interest (ROI) detection algorithms have been developed to address these issues, yet their accuracy in practical applications remains limited. This study aims to bridge this gap by integrating r-PPG with thermal imaging to enhance prediction performance. Ninety participants completed a 20-min questionnaire to induce cognitive stress, followed by watching a film aimed at eliciting moral elevation. The results demonstrate that the combination of r-PPG and thermal imaging effectively detects emotional shifts. Using r-PPG alone, the prediction accuracy was 77% for cognitive stress and 61% for moral elevation, as determined by a support vector machine (SVM). Thermal imaging alone achieved 79% accuracy for cognitive stress and 78% for moral elevation, utilizing a random forest (RF) algorithm. An early fusion strategy of these modalities significantly improved accuracies, achieving 87% for cognitive stress and 83% for moral elevation using RF. Further analysis, which utilized statistical metrics and explainable machine learning methods including SHapley Additive exPlanations (SHAP), highlighted key features and clarified the relationship between cardiac responses and facial temperature variations. Notably, it was observed that cardiovascular features derived from r-PPG models had a more pronounced influence in data fusion, despite thermal imaging's higher predictive accuracy in unimodal analysis.

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PURPOSE: Sympathetic nerve activity towards muscle (MSNA) and skin (SSNA) regulates various physiological parameters. MSNA primarily functions in blood pressure and flow, while SSNA operates in thermoregulation. Physical and cognitive stressors have been shown to have effects on both types of sympathetic activity, but there are inconsistencies as to what these effects are. This article aims to address the discrepancies in the literature and compare MSNA and SSNA responses. METHODS: Microelectrode recordings were taken from the common peroneal nerve in 29 participants: MSNA (n = 21), SSNA (n = 16) and both MSNA and SSNA (n = 8). Participants were subjected to four different 2-min stressors: two physical (isometric handgrip task, cold pressor test) and two cognitive (mental arithmetic task, Stroop colour-word conflict test), the latter of which saw participants separated into responders and non-responders to the stressors. It was hypothesised that the physical stressors would have a greater effect on MSNA than SSNA, while the cognitive stressors would operate conversely. RESULTS: Peristimulus time histogram (PSTH) analysis showed the mental arithmetic task to significantly increase both MSNA and SSNA; the isometric handgrip task and cold pressor test to increase MSNA, but not SSNA; and Stroop test to have no significant effects on changing MSNA or SSNA from baseline. Additionally, stress responses did not differ between MSNA and SSNA in participants who had both sets of data recorded. CONCLUSIONS: This study has provided evidence to support the literature which claims cognitive stressors increase sympathetic activity, and provides much needed SSNA data in response to stressors.

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Collaborative robots (cobots) have largely replaced conventional industrial robots in today's workplaces, particularly in manufacturing setups, due to their improved performance and intelligent design. In the framework of Industry 5.0, humans are working alongside cobots to accomplish the required level of automation. However, human-robot interaction has brought up concerns regarding human factors (HF) and ergonomics. A human worker may experience cognitive stress as a result of cobots' irresponsive nature in unpredictably occurring situations, which adversely affects productivity. Therefore, there is a necessity to measure stress to enhance a human worker's performance in a human-robot collaborative environment. In this study, factory workers' mental workload was assessed using physiological, behavioural, and subjective measures. Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals were collected to acquire brain signals and track hemodynamic activity, respectively. The effect of task complexity, cobot movement speed, and cobot payload capacity on the mental stress of a human worker were observed for a task designed in the context of a smart factory. Task complexity and cobot speed proved to be more impactful. As physiological measures are unbiased and more authentic means to estimate stress, eventually they may replace the other conventional measures if they prove to correlate with the results of traditional ones. Here, regression and artificial neural networks (ANN) were utilised to determine the correlation between physiological data and subjective and behavioural measures. Regression performed better for most of the targets and the best correlation (rsq-adj = 0.654146) was achieved for predicting missed beeps, a behavioural measure, using a combination of multiple EEG and fNIRS predictors. The k-nearest neighbours (KNN) algorithm was used to evaluate the accuracy of correlation between traditional measures and physiological variables, with the highest accuracy of 77.8% achieved for missed beeps as the target. Results show that physiological measures can be more insightful and have the tendency to replace other biased parameters.

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[This corrects the article DOI: 10.3389/fgene.2022.1058668.].

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Despite activities of daily living being frequently performed simultaneously with a cognitive task, motor function is often investigated in isolation, which can hinder the applicability of findings. This brief review presents evidence that 1) performing a cognitive challenge simultaneously with a motor task can negatively impact force steadiness and fatigability of limb muscles during a static contraction, 2) the negative impact on old adults (>65 years old), particularly older women is greater than young when a cognitive challenge is simultaneously performed with a static motor task, 3) age-related mechanisms potentially explain impairments in motor performance in the presence of a cognitive challenge, and 4) the mechanisms for the age-related decrements in motor performance can be distinct between men and women. These observations are highly relevant to the older adults, given the increased risk of accidents and injury when a motor task is performed with a high cognitive-demand task, especially in light of the expanding reliance on an aging workforce.

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The number of accidents by elderly drivers caused by the erroneous tread of a brake pedal or accelerator pedal has increased. A recent study reported that the number of accidents could be reduced by preparing for braking mistakes due to driving behavior by using a simulator. However, related studies have pointed out that driving behavior in simulators does not always reflect driving behavior in the real world. This paper focuses on the posture of the left foot as a behavioral precaution and provides insights into braking mistakes by comparing behavioral precautions taken on simulators and on public roads. In the experimental results, cognitive and action errors increased with age, but elderly drivers are less likely to have an accident when they are exposed to the risk of collision in situations with a mental workload by making space for the right foot to step on the brake pedal. Elderly drivers with coping skills had their left foot perpendicular to the ground and their body was unstable. This result was different from the driving behavior in the simulator, but it was not possible to identify that this difference was the cause of the collision accidents. Coping skills were predicted with 70% accuracy from the left foot posture of an elderly driver near the intersection. We expanded the system's range of use and enhanced its usefulness by predicting coping skills derived from natural driving behavior in the real world. The contributions of this study are as follows. We clarify the effect of behavioral precautions on the braking operation of elderly drivers when under a cognitive workload. We provide new insights into the use of behavioral precautions in older drivers' braking operations in the real world. We predicted coping skills from natural driving behavior near intersections in the real world.

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COVID-19 is a highly infectious disease caused by SARS-CoV-2. It causes respiratory tract infection that ranges from mild to lethal. The present study aimed to investigate the psychological impact of COVID-19 on Parkinson's disease (PD) patients. A questionnaire about the emotional, physiological, and cognitive stress symptoms was designed in the present study. A total of 94 cases and 188 controls participants filled out the questionnaire. The participants include 70.2% male and 29.8% female in both cases and controls. 27.6% of the participants were aged 18-40 years old, 33.0% were aged 41-60 years old, and 39.4% were above 61 years old. In the present study, we found that the emotional symptoms of stress were common in PD patients. Fear about own and family health was significantly higher in PD patients. A significant number of PD patients were feeling depressed; the major reason was the COVID-19 and being a PD patient, While job difficulties and COVID-19 pandemic was the main reason for feeling depressed in the control group. Constant worrying due to COVID-19 was also more common in PD patients than in the control group. Among the physiological symptoms of stress, low energy, Restlessness, clenched jaw and avoiding others were significantly higher in PD patients. Among the cognitive symptoms of stress, racing thoughts, forgetfulness, and more nervous behaviours were common in PD patients. This study concludes that PD patients face a psychological burden due to the COVID-19 pandemic, which needs proper attention.

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The most traditional sites for electrodermal activity (EDA) data collection, palmar locations such as fingers or palms, are not usually recommended for ambulatory monitoring given that subjects have to use their hands regularly during their daily activities, and therefore, alternative sites are often sought for EDA data collection. In this study, we collected EDA signals (n = 23 subjects, 19 male) from four measurement sites (forehead, back of neck, finger, and inner edge of foot) during cognitive stress and induction of mild motion artifacts by walking and one-handed weightlifting. Furthermore, we computed several EDA indices from the EDA signals obtained from different sites and evaluated their efficiency to classify cognitive stress from the baseline state. We found a high within-subject correlation between the EDA signals obtained from the finger and the feet. Consistently high correlation was also found between the finger and the foot EDA in both the phasic and tonic components. Statistically significant differences were obtained between the baseline and cognitive stress stage only for the EDA indices computed from the finger and the foot EDA. Moreover, the receiver operating characteristic curve for cognitive stress detection showed a higher area-under-the-curve for the EDA indices computed from the finger and foot EDA. We also evaluated the robustness of the different body sites against motion artifacts and found that the foot EDA location was the best alternative to other sites.

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Goal: We propose novel supervised control architectures to regulate the cognitive stress state and close the loop. Methods: We take information present in underlying neural impulses of skin conductance signals and employ model-based control techniques to close the loop in a state-space framework. For performance enhancement, we establish a supervised knowledge-based layer to update control system in real time. In the supervised architecture, the controller parameters are being updated in real-time. Results: Statistical analyses demonstrate the efficiency of supervised control architectures in improving the closed-loop results while maintaining stress levels within a desired range with more optimized control efforts. The model-based approaches would guarantee the control system-perspective criteria such as stability and optimality, and the proposed supervised knowledge-based layer would further enhance their efficiency. Conclusion: Outcomes in this in silico study verify the proficiency of the proposed supervised architectures to be implemented in the real world.

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The autonomic nervous system (ANS) is activated by stress and is closely related to the female menstrual cycle. Women with premenstrual syndrome (PMS) have an imbalanced ANS response in their premenstrual period. However, no studies have explored the reasons for the differences in ANS response among women. In this study, we investigated how the female menstrual attitude and acute social stress influence the ANS response in women with PMS. First, 277 women [24.35 ± 2.1] were selected to measure the mediating role of women's menstrual attitude between PMS severity and perceived ANS response. Second, participants' (50 women [23.23 ± 1.25] with and 46 women [22.92 ± 2.00] without PMS) heart rate (HR) and HR variability (HRV; reflecting the functioning of ANS) under social stress were measured during various menstrual cycle phases. The results indicated that menstrual attitude (bothersome and predictable) had mediating effects between the degree of PMS and perceived ANS response; when undergoing a high cognitive load (e.g. mental-arithmetic) task, the ANS of the PMS group demonstrated hypo-arousal and delayed recovery in the late luteal phase; Therefore, menstrual attitude could influence female perceived ANS response, which may be a risk factor for PMS. When women with PMS experience high-strength cognitive pressure in the premenstrual period, their ANS showed hypo-arousal and delayed recovery, which may be another risk factor for PMS.

LAY SUMMARYThis study revealed that the attitude to menstruation (bothersome and predictable) could affect the women's autonomic nervous system (ANS) response, and this may be a risk factor of premenstrual syndrome (PMS). Meanwhile, when women with PMS experience high-strength cognitive social pressure, their ANS showed hypo-arousal and delayed recovery. This imbalanced ANS reaction may result in their inability to cope with the stressful stimuli and emotional experiences, which may be another risk factor for PMS.

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We aimed at extending the repertoire of high-quality miRNA normalizers for reverse transcription-quantitative PCR (RT-qPCR) of human plasma with special emphasis on the extremely guanine-cytosine-rich portion of the miRNome. For high-throughput selection of stable candidates, microarray technology was preferred over small-RNA sequencing (sRNA-seq) since the latter underrepresented miRNAs with a guanine-cytosine (GC) content of at least 75% (p = 0.0002, n = 2). miRNA abundances measured on the microarray were ranked for consistency and uniformity using nine normalization approaches. The eleven most stable sequences included miRNAs of moderate, but also extreme GC content (45%-65%: miR-320d, miR-425-5p, miR-185-5p, miR-486-5p; 80%-95%: miR-1915-3p, miR-3656-5p, miR-3665-5p, miR-3960-5p, miR-4488-5p, miR-4497 and miR-4787-5p). In contrast, the seven extremely GC-rich miRNAs were not found in the two plasma miRNomes screened by sRNA-seq. Stem-loop RT-qPCR was employed for stability verification in 32 plasma samples of healthy male Caucasians (age range: 18-55 years). In general, inter-individual variance of miRNA abundance was low or very low as indicated by coefficient of variation (CV) values of 0.6%-8.2%. miR-3665 and miR-1915-3p outperformed in this analysis (CVs: 0.6 and 2.4%, respectively). The eight most stable sequences included four extremely GC-rich miRNAs (miR-1915-3p, miR-3665, miR-4787-5p and miR-4497). The best-performing duo normalization factor (NF) for the condition of human plasma, miR-320d and miR-4787-5p, also included a GC-extreme miRNA. In summary, the identification of extremely guanine-cytosine-rich plasma normalizers will help to increase accuracy of PCR-based miRNA quantification, thus raise the potential that miRNAs become markers for psychological stress reactions or early and precise diagnosis of clinical phenotypes. The novel miRNAs might also be useful for orthologous contexts considering their conservation in related animal genomes.

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Intensified job demands (IJDs) originate in the general accelerated pace of society and ever-changing working conditions, which subject workers to increasing workloads and deadlines, constant planning and decision-making about one's job and career, and the continual learning of new professional knowledge and skills. This study investigated how individual characteristics, namely negative and positive affectivity related to competence demands, and multitasking preference moderate the association between IJDs and cognitive stress symptoms among media workers (n = 833; 69% female, mean age 48 years). The results show that although IJDs were associated with higher cognitive stress symptoms at work, that is, difficulties in concentration, thinking clearly, decision-making, and memory, competence demands-related negative affectivity explained the most variance in cognitive stress symptoms. In addition, IJDs were more strongly associated with cognitive stress symptoms at work in individuals with high competence demand-related negative affectivity, and low multitasking preference (moderation effects). Altogether, the present findings suggest that HR practices or workplace interventions to ease employees' negative affectivity from increasing competence demands at work could usefully support employees' effective cognitive functioning when confronted with IJDs.

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Employees at healthcare organizations are experiencing more stress than ever given the current COVID-19 pandemic. Different types of stress are affecting diverse organizational outcomes, including the employees' voluntary turnover. This is the case of cognitive stress, a type of stress that affects how individuals process information, which can influence employees' turnover intentions. In this study, we look at the mechanisms that can reduce the adverse effects of cognitive stress on turnover intentions, particularly the role of employees' perceived psychological safety (i.e., how safe they perceive the interactions with their colleagues are). We hypothesize that psychological safety mediates the relationship between cognitive stress and turnover intentions, and COVID-19 worry and supervisor support moderate the relationship between cognitive stress and psychological safety. To test our hypothesis, we invited two public health care organizations in Chile to join this study. In total, we obtained a sample of 146 employees in 21 different teams. Using a multilevel model, we found that psychological safety prevents the harmful effects of cognitive stress on employees' turnover intentions. In addition, while COVID-19 worry can worsen the relationship between cognitive stress and psychological safety, supervisor support only directly affects psychological safety. This study contributes to expanding the stress and psychological safety literature and informs practitioners in healthcare organizations about how to deal with cognitive stress in the "new normality" that the pandemic has brought.

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Acute stress can have both detrimental and beneficial effects on cognitive processing, but effects on concentration performance remain unclear. Here, we investigate the effects of acute psychosocial stress on concentration performance and possible underlying physiological and psychological mechanisms. The study sample comprised 47 healthy male participants who were randomly assigned either to a psychosocial stress situation (Trier Social Stress Test) or a neutral control task. Concentration performance was assessed using the d2 Test of Attention before and 30 min after the stress or control task. Salivary cortisol and alpha-amylase were repeatedly measured before and up to 1 hr after stress. We repeatedly assessed state anxiety using the State-Trait Anxiety Inventory and anticipatory cognitive stress appraisal using the Primary Appraisal Secondary Appraisal questionnaire. The stress group showed a significantly stronger improvement of concentration performance compared to the control group (p = .042). Concentration performance improvement was predicted by increased state anxiety (p = .020) and lower cortisol (stress) changes (p = .043). Neither changes in alpha-amylase nor cognitive stress appraisal did relate to concentration performance. Our results show improved concentration performance after acute psychosocial stress induction that was predicted by higher state anxiety increases and lower cortisol increases. This points to a potential modulating role of specific psycho-emotional and physiological factors with opposite effects.

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In this study, a novel classification method for a driver's cognitive stress level was proposed, whereby the interbeat intervals extracted from an electrocardiogram (ECG) signal were transferred to pictures, and a convolution neural network (CNN) was used to train the pictures to classify a driver's cognitive stress level. First, we defined three levels of tasks and collected the ECG signal of the driver at different cognitive stress levels by designing and performing a driving simulation experiment. We extracted the interbeat intervals and converted them to pictures according to the number of consecutive interbeat intervals in each picture. Second, the CNN model was used to train the data set to recognize the cognitive stress levels. Classification accuracies of 100%, 91.6% and 92.8% were obtained for the training set, validation set and test set, respectively, and were compared with those the BP neural network. Last, we discussed the influence of the number of interbeat intervals in each picture on the performance of the proposed classification method. The results showed that the performance initially improved with an increase in the number of interbeat intervals. A downward trend was observed when the number exceeded 40, and when the number was 40, the model performed best with the highest accuracy (98.79%) and a relatively low relative standard deviation (0.019).

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This work used a low-cost wireless electroencephalography (EEG) headset to quantify the human response to different cognitive stress states on a single-trial basis. We used a Stroop-type color⁻word interference test to elicit mild stress responses in 18 subjects while recording scalp EEG. Signals recorded from thirteen scalp locations were analyzed using an algorithm that computes the root mean square voltages in the theta (4⁻8 Hz), alpha (8⁻13 Hz), and beta (13⁻30 Hz) bands immediately following the initiation of Stroop stimuli; the mean of the Teager energy in each of these three bands; and the wideband EEG signal line-length and number of peaks. These computational features were extracted from the EEG signals on thirteen electrodes during each stimulus presentation and used as inputs to logistic regression, quadratic discriminant analysis, and k-nearest neighbor classifiers. Two complementary analysis methodologies indicated classification accuracies over subjects of around 80% on a balanced dataset for the logistic regression classifier when information from all electrodes was taken into account simultaneously. Additionally, we found evidence that stress responses were preferentially time-locked to stimulus presentation, and that certain electrode⁻feature combinations worked broadly well across subjects to distinguish stress states.

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Sex differences in the study of the human physiological response to mental stress are often erroneously ignored. To this end, we set out to show that our understanding of the stress response is fundamentally altered once sex differences are taken into account. This is achieved by comparing the heart rate variability (HRV) signals acquired during mental maths tests from ten females and ten males of similar maths ability; all females were in the follicular phase of their menstrual cycle. For rigor, the HRV signals from this pilot study were analyzed using temporal, spectral and nonlinear signal processing techniques, which all revealed significant statistical differences between the sexes, with the stress-induced increases in the heart rates from the males being significantly larger than those from the females (p-value = 4.4 × 10-4). In addition, mental stress produced an overall increase in the power of the low frequency component of HRV in the males, but caused an overall decrease in the females. The stress-induced changes in the power of the high frequency component were even more profound; it greatly decreased in the males, but increased in the females. We also show that mental stress was followed by the expected decrease in sample entropy, a nonlinear measure of signal regularity, computed from the males' HRV signals, while overall, stress manifested in an increase in the sample entropy computed from the females' HRV signals. This finding is significant, since mental stress is commonly understood to be manifested in the decreased entropy of HRV signals. The significant difference (p-value = 2.1 × 10-9) between the changes in the entropies from the males and females highlights the pitfalls in ignoring sex in the formation of a physiological hypothesis. Furthermore, it has been argued that estrogen attenuates the effect of catecholamine stress hormones; the findings from this investigation suggest for the first time that the conventionally cited cardiac changes, attributed to the fight-or-flight stress response, are not universally applicable to females. Instead, this pilot study provides an alternative interpretation of cardiac responses to stress in females, which indicates a closer alignment to the evolutionary tend-and-befriend response.