Stress assessment by means of heart rate derived from functional near-infrared spectroscopy

Cerebral hemodynamic and systemic physiological changes in trained freedivers completing sled-assisted dives to two different depths

Although existing literature covers significant detail on the physiology of human freediving, the lack of standardized protocols has hindered comparisons due to confounding variables such as exercise and depth. By accounting for these variables, direct depth-dependent impacts on cardiovascular and blood oxygen regulation can be investigated. In this study, depth-dependent effects on 1) cerebral hemodynamic and oxygenation changes, 2) arterial oxygen saturation ([Formula: see text]), and 3) heart rate during breath-hold diving without confounding effects of exercise were investigated. Six freedivers (51.0 ± 12.6 yr; means ± SD), instrumented with continuous-wave near-infrared spectroscopy for monitoring cerebral hemodynamic and oxygenation measurements, heart rate, and [Formula: see text], performed sled-assisted breath-hold dives to 15 m and 42 m. Arterial blood gas tensions were validated through cross-sectional periodic blood sampling. Cerebral hemodynamic changes were characteristic of breath-hold diving, with changes during ascent from both depths likely driven by decreasing [Formula: see text] due to lung expansion. Although [Formula: see text] was significantly lower following 42-m dives [t(5) = -4.183, P < 0.05], mean cerebral arterial-venous blood oxygen saturation remained at 74% following dives to both depths. Cerebral oxygenation during ascent from 42 m may have been maintained through increased arterial delivery. Heart rate was variable with no significant difference in minimum heart rate between both depths [t(5) = -1.017, P > 0.05]. This study presents a standardized methodology, which could provide a basis for future research on human freediving physiology and uncover ways in which freedivers can reduce potential risks of the sport.NEW & NOTEWORTHY We present a standardized methodology in which trained breath-hold divers instrumented with wearable near-infrared spectroscopy (NIRS) technology and a cannula for arterial blood sampling completed sled-assisted dives to two different dive depths to account for the confounding factors of exercise and depth during breath-hold diving. In our investigation, we highlight the utility of wearable NIRS systems for continuous hemodynamic and oxygenation monitoring to investigate the impacts of hydrostatic pressure on cardiovascular and blood oxygen regulation.

Near-Infrared Spectroscopy for Neonatal Sleep Classification

Sleep, notably active sleep (AS) and quiet sleep (QS), plays a pivotal role in the brain development and gradual maturation of (pre) term infants. Monitoring their sleep patterns is imperative, as it can serve as a tool in promoting neurological maturation and well-being, particularly important in preterm infants who are at an increased risk of immature brain development. An accurate classification of neonatal sleep states can contribute to optimizing treatments for high-risk infants, with respiratory rate (RR) and heart rate (HR) serving as key components in sleep assessment systems for neonates. Recent studies have demonstrated the feasibility of extracting both RR and HR using near-infrared spectroscopy (NIRS) in neonates. This study introduces a comprehensive sleep classification approach leveraging high-frequency NIRS signals recorded at a sampling rate of 100 Hz from a cohort of nine preterm infants admitted to a neonatal intensive care unit. Eight distinct features were extracted from the raw NIRS signals, including HR, RR, motion-related parameters, and proxies for neural activity. These features served as inputs for a deep convolutional neural network (CNN) model designed for the classification of AS and QS sleep states. The performance of the proposed CNN model was evaluated using two cross-validation approaches: ten-fold cross-validation of data pooling and five-fold cross-validation, where each fold contains two independently recorded NIRS data. The accuracy, balanced accuracy, F1-score, Kappa, and AUC-ROC (Area Under the Curve of the Receiver Operating Characteristic) were employed to assess the classifier performance. In addition, comparative analyses against six benchmark classifiers, comprising K-Nearest Neighbors, Naive Bayes, Support Vector Machines, Random Forest (RF), AdaBoost, and XGBoost (XGB), were conducted. Our results reveal the CNN model's superior performance, achieving an average accuracy of 88%, a balanced accuracy of 94%, an F1-score of 91%, Kappa of 95%, and an AUC-ROC of 96% in data pooling cross-validation. Furthermore, in both cross-validation methods, RF and XGB demonstrated accuracy levels closely comparable to the CNN classifier. These findings underscore the feasibility of leveraging high-frequency NIRS data, coupled with NIRS-based HR and RR extraction, for assessing sleep states in neonates, even in an intensive care setting. The user-friendliness, portability, and reduced sensor complexity of the approach suggest its potential applications in various less-demanding settings. This research thus presents a promising avenue for advancing neonatal sleep assessment and its implications for infant health and development.

Functional near-infrared spectroscopy based blood pressure variations and hemodynamic activity of brain monitoring following postural changes: A systematic review

Postural change from supine or sitting to standing up leads to displacement of 300 to 1000 mL of blood from the central parts of the body to the lower limb, which causes a decrease in venous return to the heart, hence decrease in cardiac output, causing a drop in blood pressure. This may lead to falling down, syncope, and in general reducing the quality of daily activities, especially in the elderly and anyone suffering from nervous system disorders such as Parkinson's or orthostatic hypotension (OH). Among different modalities to study brain function, functional near-infrared spectroscopy (fNIRS) is a neuroimaging method that optically measures the hemodynamic response in brain tissue. Concentration changes in oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (HHb) are associated with brain neural activity. fNIRS is significantly more tolerant to motion artifacts compared to fMRI, PET, and EEG. At the same time, it is portable, has a simple structure and usage, is safer, and much more economical. In this article, we systematically reviewed the literature to examine the history of using fNIRS in monitoring brain oxygenation changes caused by sudden changes in body position and its relationship with the blood pressure changes. First, the theory behind brain hemodynamics monitoring using fNIRS and its advantages and disadvantages are presented. Then, a study of blood pressure variations as a result of postural changes using fNIRS is described. It is observed that only 58 % of the references concluded a positive correlation between brain oxygenation changes and blood pressure changes. At the same time, 3 % showed a negative correlation, and 39 % did not show any correlation between them.

Real-time mental stress detection using multimodality expressions with a deep learning framework

Mental stress is becoming increasingly widespread and gradually severe in modern society, threatening people’s physical and mental health. To avoid the adverse effects of stress on people, it is imperative to detect stress in time. Many studies have demonstrated the effectiveness of using objective indicators to detect stress. Over the past few years, a growing number of researchers have been trying to use deep learning technology to detect stress. However, these works usually use single-modality for stress detection and rarely combine stress-related information from multimodality. In this paper, a real-time deep learning framework is proposed to fuse ECG, voice, and facial expressions for acute stress detection. The framework extracts the stress-related information of the corresponding input through ResNet50 and I3D with the temporal attention module (TAM), where TAM can highlight the distinguishing temporal representation for facial expressions about stress. The matrix eigenvector-based approach is then used to fuse the multimodality information about stress. To validate the effectiveness of the framework, a well-established psychological experiment, the Montreal imaging stress task (MIST), was applied in this work. We collected multimodality data from 20 participants during MIST. The results demonstrate that the framework can combine stress-related information from multimodality to achieve 85.1% accuracy in distinguishing acute stress. It can serve as a tool for computer-aided stress detection.

Frequency-domain analysis of fNIRS fluctuations induced by rhythmic mental arithmetic

Functional near‐infrared spectroscopy (fNIRS) is an increasingly used technology for imaging neural correlates of cognitive processes. However, fNIRS signals are commonly impaired by task‐evoked and spontaneous hemodynamic oscillations of non‐cerebral origin, a major challenge in fNIRS research. In an attempt to isolate the task‐evoked cortical response, we investigated the coupling between hemodynamic changes arising from superficial and deep layers during mental effort. For this aim, we applied a rhythmic mental arithmetic task to induce cyclic hemodynamic fluctuations suitable for effective frequency‐resolved measurements. Twenty university students aged 18–25 years (eight males) underwent the task while hemodynamic changes were monitored in the forehead using a newly developed NIRS device, capable of multi‐channel and multi‐distance recordings. We found significant task‐related fluctuations for oxy‐ and deoxy‐hemoglobin, highly coherent across shallow and deep tissue layers, corroborating the strong influence of surface hemodynamics on deep fNIRS signals. Importantly, after removing such surface contamination by linear regression, we show that the frontopolar cortex response to a mental math task follows an unusual inverse oxygenation pattern. We confirm this finding by applying for the first time an alternative method to estimate the neural signal, based on transfer function analysis and phasor algebra. Altogether, our results demonstrate the feasibility of using a rhythmic mental task to impose an oscillatory state useful to separate true brain functional responses from those of non‐cerebral origin. This separation appears to be essential for a better understanding of fNIRS data and to assess more precisely the dynamics of the neuro‐visceral link.

We proposed the use of rhythmic mental arithmetic tasks to induce cyclic oscillations in multi‐distance fNIRS signals measured on the forehead, suitable for effective frequency‐domain analysis to better identify the actual neural functional response. We confirm the impairment of deep signals by task‐evoked non‐cerebral confounds, while providing evidence for an inverse oxygenation response in the frontopolar cortex.

Brain Mechanisms of Virtual Reality Breathing Versus Traditional Mindful Breathing in Pain Modulation: Observational Functional Near-infrared Spectroscopy Study

BACKGROUND

Pain is a complex experience that involves sensory-discriminative and cognitive-emotional neuronal processes. It has long been known across cultures that pain can be relieved by mindful breathing (MB). There is a common assumption that MB exerts its analgesic effect through interoception. Interoception refers to consciously refocusing the mind's attention to the physical sensation of internal organ function.

OBJECTIVE

In this study, we dissect the cortical analgesic processes by imaging the brains of healthy subjects exposed to traditional MB (TMB) and compare them with another group for which we augmented MB to an outside sensory experience via virtual reality breathing (VRB).

METHODS

The VRB protocol involved in-house-developed virtual reality 3D lungs that synchronized with the participants' breathing cycles in real time, providing them with an immersive visual-auditory exteroception of their breathing.

RESULTS

We found that both breathing interventions led to a significant increase in pain thresholds after week-long practices, as measured by a thermal quantitative sensory test. However, the underlying analgesic brain mechanisms were opposite, as revealed by functional near-infrared spectroscopy data. In the TMB practice, the anterior prefrontal cortex uniquely modulated the premotor cortex. This increased its functional connection with the primary somatosensory cortex (S1), thereby facilitating the S1-based sensory-interoceptive processing of breathing but inhibiting its other role in sensory-discriminative pain processing. In contrast, virtual reality induced an immersive 3D exteroception with augmented visual-auditory cortical activations, which diminished the functional connection with the S1 and consequently weakened the pain processing function of the S1.

CONCLUSIONS

In summary, our study suggested two analgesic neuromechanisms of VRB and TMB practices-exteroception and interoception-that distinctively modulated the S1 processing of the ascending noxious inputs. This is in line with the concept of dualism (Yin and Yang).

When the human brain goes diving: using near-infrared spectroscopy to measure cerebral and systemic cardiovascular responses to deep, breath-hold diving in elite freedivers

Continuous measurements of haemodynamic and oxygenation changes in free living animals remain elusive. However, developments in biomedical technologies may help to fill this knowledge gap. One such technology is continuous-wave near-infrared spectroscopy (CW-NIRS)-a wearable and non-invasive optical technology. Here, we develop a marinized CW-NIRS system and deploy it on elite competition freedivers to test its capacity to function during deep freediving to 107 m depth. We use the oxyhaemoglobin and deoxyhaemoglobin concentration changes measured with CW-NIRS to monitor cerebral haemodynamic changes and oxygenation, arterial saturation and heart rate. Furthermore, using concentration changes in oxyhaemoglobin engendered by cardiac pulsation, we demonstrate the ability to conduct additional feature exploration of cardiac-dependent haemodynamic changes. Freedivers showed cerebral haemodynamic changes characteristic of apnoeic diving, while some divers also showed considerable elevations in venous blood volumes close to the end of diving. Some freedivers also showed pronounced arterial deoxygenation, the most extreme of which resulted in an arterial saturation of 25%. Freedivers also displayed heart rate changes that were comparable to diving mammals both in magnitude and patterns of change. Finally, changes in cardiac waveform associated with heart rates less than 40 bpm were associated with changes indicative of a reduction in vascular compliance. The success here of CW-NIRS to non-invasively measure a suite of physiological phenomenon in a deep-diving mammal highlights its efficacy as a future physiological monitoring tool for human freedivers as well as free living animals. This article is part of the theme issue 'Measuring physiology in free-living animals (Part II)'.

Neuroprotection of the Perinatal Brain by Early Information of Cerebral Oxygenation and Perfusion Patterns

Abnormal patterns of cerebral perfusion/oxygenation are associated with neuronal damage. In preterm neonates, hypoxemia, hypo-/hypercapnia and lack of cerebral autoregulation are related to peri-intraventricular hemorrhages and white matter injury. Reperfusion damage after perinatal hypoxic ischemia in term neonates seems related with cerebral hyperoxygenation. Since biological tissue is transparent for near infrared (NIR) light, NIR-spectroscopy (NIRS) is a noninvasive bedside tool to monitor brain oxygenation and perfusion. This review focuses on early assessment and guiding abnormal cerebral oxygenation/perfusion patterns to possibly reduce brain injury. In term infants, early patterns of brain oxygenation helps to decide whether or not therapy (hypothermia) and add-on therapies should be considered. Further NIRS-related technical advances such as the use of (functional) NIRS allowing simultaneous estimation and integrating of heart rate, respiration rate and monitoring cerebral autoregulation will be discussed.

Signal quality index: an algorithm for quantitative assessment of functional near infrared spectroscopy signal quality

We propose the signal quality index (SQI) algorithm as a novel tool for quantitatively assessing the functional near infrared spectroscopy (fNIRS) signal quality in a numeric scale from 1 (very low quality) to 5 (very high quality). The algorithm comprises two preprocessing steps followed by three consecutive rating stages. The results on a dataset annotated by independent fNIRS experts showed SQI performed significantly better (p<0.05) than PHOEBE (placing headgear optodes efficiently before experimentation) and SCI (scalp coupling index), two existing algorithms, in both quantitatively rating and binary classifying the fNIRS signal quality. Employment of the proposed algorithm to estimate the signal quality before processing the fNIRS signals increases certainty in the interpretations.

Proposing a convolutional neural network for stress assessment by means of derived heart rate from functional near infrared spectroscopy

BACKGROUND

Stress is known as one of the major factors threatening human health. A large number of studies have been performed in order to either assess or relieve stress by analyzing the brain and heart-related signals.

METHOD

In this study, a method based on the Convolutional Neural Network (CNN) approach is proposed to assess stress induced by the Montreal Imaging Stress Task. The proposed model is trained on the heart rate signal derived from functional Near-Infrared Spectroscopy (fNIRS), which is referred to as HRF. In this regard, fNIRS signals of 20 healthy volunteers were recorded using a configuration of 23 channels located on the prefrontal cortex. The proposed deep learning system consists of two main parts where in the first part, the one-dimensional convolutional neural network is employed to build informative activation maps, and then in the second part, a stack of deep fully connected layers is used to predict the stress existence probability. Thereafter, the employed CNN method is compared with the Dense Neural Network, Support Vector Machine, and Random Forest regarding various classification metrics.

RESULTS

Results clearly showed the superiority of CNN over all other methods. Additionally, the trained HRF model significantly outperforms the model trained on the filtered fNIRS signals, where the HRF model could achieve 98.69 ± 0.45% accuracy, which is 10.09% greater than the accuracy obtained by the fNIRS model.

CONCLUSIONS

Employment of the proposed deep learning system trained on the HRF measurements leads to higher stress classification accuracy than the accuracy reported in the existing studies where the same experimental procedure has been done. Besides, the proposed method suggests better stability with lower variation in prediction. Furthermore, its low computational cost opens up the possibility to be applied in real-time monitoring of stress assessment.