Estimation of heart rate signals for mental stress assessment using neuro fuzzy technique

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.

Assessment of mental workload based on multi-physiological signals

BACKGROUND:

Mental workload is one of the contributing factors to human errors in road accidents or other potentially adverse incidents.

OBJECTIVE:

This research probes the effects of mental workload on the electroencephalographic (EEG) and electrocardiogram (ECG) of subjects in visual monitoring tasks, based on which a comprehensive evaluation model for mental workload is established effectively.

METHODS:

Three degrees of mental workload were obtained by monitoring tasks with different levels of difficulty. 20 healthy subjects were selected to take part in the research.

RESULTS:

The subjective scores showed a significant increase with the increase of task difficulty, meanwhile the reaction time (RT) increased and the accuracy decreased significantly, which proved the validity of three degrees of mental workload induced. For the EEG parameters, a significant decrease of θ energy was found in Frontal, Parietal and Occipital with the increase of level of mental workload, as well as a significant decrease of α energy in Frontal, Central and Occipital, meanwhile a significant increase of β energy occurred in Frontal and Occipital. There was a significant decrease of α/θ in Occipital, and significant increases of θ/β and (α+β)/θ in Frontal, Central and Occipital, meanwhile (α+θ)/β and WPE decreased significantly in Frontal and Occipital. Among the ECG parameters, it was shown that Mean RR, RMSSD, HF_norm and SampEn decreased significantly with the increase of task difficulty, while LF_norm and LF/HF showed significant increases. These EEG indictors in Occipital and ECG indictors were chosen and constituted a multidimensional original sample. Principal Component Analysis (PCA) was used to extract the principal elements and decreased the dimension of sample space in order to simplify the calculation, based on which an effective classification model with accuracy of 80% was achieved by support vector machine (SVM).

CONCLUSION:

This study demonstrates that the proposed algorithm can be applied to mental workload monitoring.

A Flexible Wearable Device for Measurement of Cardiac, Electrodermal, and Motion Parameters in Mental Healthcare Applications

Mental illnesses are vast and cause a lot of individual and social discomfort, with significant healthcare costs associated in terms of diagnosis and treatment. They can be triggered by a number of factors including stress, fatigue or anxiety. The associated physiological, cardiac and autonomic changes can be assessed, centrally, through brain imaging or, peripherally, by other signal recording modalities. With recent advances in wearable devices, these parameters can now be assessed in natural living conditions as associated mood disorders such as obsessive/compulsive behavior or depression are difficult to be examined in controlled settings. In this paper, we present a low-powered and flexible device with electrocardiogram (ECG), galvanic skin response (GSR), temperature and bio-motion detection channels, with signal accuracies of 62 μV for ECG, 6.6 kΩ for GSR, 0.13 °C for temperature and 0.04 g for acceleration. Potential applications include mental health assessment of patients during daily activities at home and/or work through non-continuous and multimodal sensing as demonstrated in this paper during exercise, rest and mental activities performed by healthy individuals only, achieving an overall accuracy of 89% in the classification of the different tasks executed by volunteers.

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

Many studies have been carried out in order to detect and quantify the level of mental stress by means of different physiological signals. From the physiological point of view, stress promptly affects brain and cardiac function; therefore, stress can be assessed by analyzing the brain- and heart-related signals more efficiently. Signals produced by functional near-infrared spectroscopy (fNIRS) of the brain together with the heart rate (HR) are employed to assess the stress induced by the Montreal Imaging Stress Task. Two different versions of the HR are used in this study. The first one is the commonly used HR derived from the electrocardiogram (ECG) and is considered as the reference HR (RHR). The other is the HR computed from the fNIRS signal (EHR) by means of an effective combinational algorithm. fNIRS and ECG signals were simultaneously recorded from 10 volunteers, and EHR and RHR are derived from them, respectively. Our results showed a high degree of agreement [r  >  0.9, BAR (Bland Altman ratio) <5  %  ] between the two HR. A principal component analysis/support vector machine-based algorithm for stress classification is developed and applied to the three measurements of fNIRS, EHR, and RHR and a classification accuracy of 78.8%, 94.6%, and 62.2% were obtained for the three measurements, respectively. From these observations, it can be concluded that the EHR carries more useful information with regards to the mental stress than the RHR and fNIRS signals. Therefore, EHR can be used alone or in combination with the fNIRS signal for a more accurate and real-time stress detection and classification.

Portable System for Real-Time Detection of Stress Level

Currently, mental stress is a major problem in our society. It is related to a wide variety of diseases and is mainly caused by daily-life factors. The use of mobile technology for healthcare purposes has dramatically increased during the last few years. In particular, for out-of-lab stress detection, a considerable number of biosignal-based methods and systems have been proposed. However, these approaches have not matured yet into applications that are reliable and useful enough to significantly improve people’s quality of life. Further research is needed. In this paper, we propose a portable system for real-time detection of stress based on multiple biosignals such as electroencephalography, electrocardiography, electromyography, and galvanic skin response. In order to validate our system, we conducted a study using a previously published and well-established methodology. In our study, ten subjects were stressed and then relaxed while their biosignals were simultaneously recorded with the portable system. The results show that our system can classify three levels of stress (stress, relax, and neutral) with a resolution of a few seconds and 86% accuracy. This suggests that the proposed system could have a relevant impact on people’s lives. It can be used to prevent stress episodes in many situations of everyday life such as work, school, and home.

Stress Assessment by Prefrontal Relative Gamma

Stress assessment has been under study in the last years. Both biochemical and physiological markers have been used to measure stress level. In neuroscience, several studies have related modification of stress level to brain activity changes in limbic system and frontal regions, by using non-invasive techniques such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). In particular, previous studies suggested that the exhibition or inhibition of certain brain rhythms in frontal cortical areas indicates stress. However, there is no established marker to measure stress level by EEG. In this work, we aimed to prove the usefulness of the prefrontal relative gamma power (RG) for stress assessment. We conducted a study based on stress and relaxation periods. Six healthy subjects performed the Montreal Imaging Stress Task (MIST) followed by a stay within a relaxation room while EEG and electrocardiographic signals were recorded. Our results showed that the prefrontal RG correlated with the expected stress level and with the heart rate (HR; 0.8). In addition, the difference in prefrontal RG between time periods of different stress level was statistically significant (p < 0.01). Moreover, the RG was more discriminative between stress levels than alpha asymmetry, theta, alpha, beta, and gamma power in prefrontal cortex. We propose the prefrontal RG as a marker for stress assessment. Compared with other established markers such as the HR or the cortisol, it has higher temporal resolution. Additionally, it needs few electrodes located at non-hairy head positions, thus facilitating the use of non-invasive dry wearable real-time devices for ubiquitous assessment of stress.

An EEG-based mental workload estimator trained on working memory task can work well under simulated multi-attribute task

Mental workload (MW)-based adaptive system has been found to be an effective approach to enhance the performance of human-machine interaction and to avoid human error caused by overload. However, MW estimated from the spontaneously generated electroencephalogram (EEG) was found to be task-specific. In existing studies, EEG-based MW classifier can work well under the task used to train the classifier (within-task) but crash completely when used to classify MW of a task that is similar to but not included in the training data (cross-task). The possible causes have been considered to be the task-specific EEG patterns, the mismatched workload across tasks and the temporal effects. In this study, cross-task performance-based feature selection (FS) and regression model were tried to cope with these challenges, in order to make EEG-based MW estimator trained on working memory tasks work well under a complex simulated multi-attribute task (MAT). The results show that the performance of regression model trained on working memory task and tested on multi-attribute task with the feature subset picked-out were significantly improved (correlation coefficient (COR): 0.740 ± 0.147 and 0.598 ± 0.161 for FS data and validation data respectively) when compared to the performance in the same condition with all features (chance level). It can be inferred that there do exist some MW-related EEG features can be picked out and there are something in common between MW of a relatively simple task and a complex task. This study provides a promising approach to measure MW across tasks.