Stress Resilience Measurement With Heart-Rate Variability During Mental And Physical Stress

Short-term effects of a maqui-based nutraceutical on heart rate variability, psycho-physical resilience and on a few metabolic biomarkers: a randomized controlled study

OBJECTIVES

To assess the immediate/short-term outcomes of a maqui berry-based nutraceutical (MBN) supplementation on the autonomic nervous system (ANS), resilience level, metabolism and body composition.

METHODS

A randomized controlled single-blind study was performed on healthy subjects. Throughout 30 days, one group took 1,000 mg/day of an MBN (Maqui 500^®), the control group had no supplementation. On day 0 (T0) and 30 (T3) all subjects performed blood tests, bioimpedance spectroscopy and questionnaires for resilience, perceived stress and short-form 12 (SF12). At T0, 75' after T0 (T1), on day 7 and at T3 the subjects underwent biometric parameter measurement and heart rate variability (HRV) test to investigate psycho-physical resilience.

RESULTS

Fifteen subjects per group were included; abnormal seasonal high temperatures altered individuals' lifestyle and nutrition, influencing the trial's outcomes. Biometric parameters, blood pressure, oxygen saturation and blood tests did not differ between T0 and T3 in both groups. In the MBN group the HRV analysis showed a significant increase of ANS coordination (p=0.05), parasympathetic activity at 75', very low frequencies and total power at T3, whereas these parameters decreased in the control group. SF12 mental score improved in the maqui group (p=0.02); the questionnaire-based outcomes showed no further variations. In the control subjects bioimpedance showed an increase of resistance and fat mass, with decreased total body water and lean mass (p=n.s.).

CONCLUSIONS

The maqui-based nutraceutical improved HRV, namely ANS activation/coordination, and SF12 mental component. Blood tests and bioimpedance/biometric parameters mildly varied. The elapsed hot weather likely biased many investigated variables.

Interactive Cardio System for Healthcare Improvement

The paper presents an interactive cardio system that can be used to improve healthcare. The proposed system receives, processes, and analyzes cardio data using an Internet-based software platform. The system enables the acquisition of biomedical data using various means of recording cardiac signals located in remote locations around the world. The recorded discretized cardio information is transmitted to the system for processing and mathematical analysis. At the same time, the recorded cardio data can also be stored online in established databases. The article presents the algorithms for the preprocessing and mathematical analysis of cardio data (heart rate variability). The results of studies conducted on the Holter recordings of healthy individuals and individuals with cardiovascular diseases are presented. The created system can be used for the remote monitoring of patients with chronic cardiovascular diseases or patients in remote settlements (where, for example, there may be no hospitals), control and assistance in the process of treatment, and monitoring the taking of prescribed drugs to help to improve people's quality of life. In addition, the issue of ensuring the security of cardio information and the confidentiality of the personal data of health users is considered.

Comparing a Fitbit Wearable to an Electrocardiogram Gold Standard as a Measure of Heart Rate Under Psychological Stress: A Validation Study

BACKGROUND

Wearable devices collect physiological and behavioral data that have the potential to identify individuals at risk of declining mental health and well-being. Past research has mainly focused on assessing the accuracy and the agreement of heart rate (HR) measurement of wearables under different physical exercise conditions. However, the capacity of wearables to sense physiological changes, assessed by increasing HR, caused by a stressful event has not been thoroughly studied.

OBJECTIVE

This study followed 3 objectives: (1) to test the ability of a wearable device (Fitbit Versa 2) to sense an increase in HR upon induction of psychological stress in the laboratory; (2) to assess the accuracy of the wearable device to capture short-term HR variations caused by psychological stress compared to a gold-standard electrocardiogram (ECG) measure (Biopac); and (3) to quantify the degree of agreement between the wearable device and the gold-standard ECG measure across different experimental conditions.

METHODS

Participants underwent the Trier Social Stress Test protocol, which consists of an oral phase, an arithmetic stress phase, an anticipation phase, and 2 relaxation phases (at the beginning and the end). During the stress protocol, the participants wore a Fitbit Versa 2 and were also connected to a Biopac. A mixed-effect modeling approach was used (1) to assess the effect of experimental conditions on HR, (2) to estimate several metrics of accuracy, and (3) to assess the agreement: the Bland-Altman limits of agreement (LoA), the concordance correlation coefficient, the coverage probability, the total deviation index, and the coefficient of an individual agreement. Mean absolute error and mean absolute percent error were calculated as accuracy indices.

RESULTS

A total of 34 university students were recruited for this study (64% of participants were female with a mean age of 26.8 years, SD 8.3). Overall, the results showed significant HR variations across experimental phases. Post hoc tests revealed significant pairwise differences for all phases. Accuracy analyses revealed acceptable accuracy according to the analyzed metrics of accuracy for the Fitbit Versa 2 to capture the short-term variations in psychological stress levels. However, poor indices of agreement between the Fitbit Versa 2 and the Biopac were found.

CONCLUSIONS

Overall, the results support the use of the Fitbit Versa 2 to capture short-term stress variations. The Fitbit device showed acceptable levels of accuracy but poor agreement with an ECG gold standard. Greater inaccuracy and smaller agreement were found for stressful experimental conditions that induced a higher HR. Fitbit devices can be used in research to measure HR variations caused by stress, although they cannot replace an ECG instrument when precision is of utmost importance.

Heart rate variability and cortisol levels in school-age children with different cognitive tests

Background. An urgent task of age-related physiology is to study the functional state of the body of school-age children in cognitive activity due to the large academic load and the use of information and computer technologies in the educational process to identify the characteristics of the reactivity of the body of students when performing cognitive load of various types, including on electronic devices, is necessary for the organization of the school educational environment.The aim. To assess the nature of vegetative, cardiovascular and hormonal reactivity in cognitive load of various types in school-age children.Materials and methods. By methods of heart rate variability analysis, electrocardiography, tonometry and enzyme immunoassay of cortisol in saliva, 117 school-age children were examined while performing cognitive load of various types.Results. There is a change in heart rate variability indicators while performing cognitive load. Oral counting causes an increase in sympathetic influences on the heart rate with a decrease in parasympathetic activity, as well as a shift in the vagosympathetic balance. Operation on the electronic devices causes a decrease in the total power density of the spectrum and an increase in the index of low-frequency and highfrequency vibrations ratio due to a decrease in parasympathetic activity. Two types of reaction were revealed: type I – an increase in the concentration of cortisol in saliva, an increase in sympathetic effects on Heart rate with a simultaneous decrease in parasympathetic activity (counting), a decrease in the total power of the spectrum density (laptop), a decrease in parasympathetic activity (tablet). Type II – a decrease in hormone levels and a decrease in very low-frequency vibrations and parasympathetic activity, regardless of the type of load presentedConclusion. The results obtained indicate that the nature of the reactivity of heart rate indicators and the stress hormone cortisol in students depends not so much on which electronic device it is performed on, but on the type of cognitive load.

Preprocessing Methods for Ambulatory HRV Analysis Based on HRV Distribution, Variability and Characteristics (DVC)

Thanks to wearable devices joint with AI algorithms, it is possible to record and analyse physiological parameters such as heart rate variability (HRV) in ambulatory environments. The main downside to such setups is the bad quality of recorded data due to movement, noises, and data losses. These errors may considerably alter HRV analysis and should therefore be addressed beforehand, especially if used for medical diagnosis. One widely used method to handle such problems is interpolation, but this approach does not preserve the time dependence of the signal. In this study, we propose a new method for HRV processing including filtering and iterative data imputation using a Gaussian distribution. The particularity of the method is that many physiological aspects are taken into consideration, such as HRV distribution, RR variability, and normal boundaries, as well as time series characteristics. We study the effect of this method on classification using a random forest classifier (RF) and compare it to other data imputation methods including linear, shape-preserving piecewise cubic Hermite (pchip), and spline interpolation in a case study on stress. Features from reconstructed HRV signals of 67 healthy subjects using all four methods were analysed and separately classified by a random forest algorithm to detect stress against relaxation. The proposed method reached a stable F1 score of 61% even with a high percentage of missing data, whereas other interpolation methods reached approximately 54% F1 score for a low percentage of missing data, and the performance drops to about 44% when the percentage is increased. This suggests that our method gives better results for stress classification, especially on signals with a high percentage of missing data.

Respiratory Event Detection during Sleep Using Electrocardiogram and Respiratory Related Signals: Using Polysomnogram and Patch-Type Wearable Device Data

This paper presents an automatic algorithm for the detection of respiratory events in patients using electrocardiogram (ECG) and respiratory signals. The proposed method was developed using data of polysomnogram (PSG) and those recorded from a patch-type device. In total, data of 1,285 subjects were used for algorithm development and evaluation. The proposed method involved respiratory event detection and apnea-hypopnea index (AHI) estimation. Handcrafted features from the ECG and respiratory signals were applied to machine learning algorithms including linear discriminant analysis, quadratic discriminant analysis, random forest, multi-layer perceptron, and the support vector machine (SVM). High performance was demonstrated when using SVM, where the overall accuracy achieved was 83% and the Cohens kappa was 0.53 for the minute-by-minute respiratory event detection. The correlation coefficient between the reference AHI obtained using the PSG and estimated AHI as per the proposed method was 0.87. Furthermore, patient classification based on an AHI cutoff of 15 showed an accuracy of 87% and a Cohens kappa of 0.72. The proposed method increases performance result, as it records the ECG and respiratory signals simultaneously. Overall, it can be used to lower the development cost of commercial software owing to the use of open datasets.

Classification of Mental Stress Using CNN-LSTM Algorithms with Electrocardiogram Signals

The mental stress faced by many people in modern society is a factor that causes various chronic diseases, such as depression, cancer, and cardiovascular disease, according to stress accumulation. Therefore, it is very important to regularly manage and monitor a person's stress. In this study, we propose an ensemble algorithm that can accurately determine mental stress states using a modified convolutional neural network (CNN)- long short-term memory (LSTM) architecture. When a person is exposed to stress, a displacement occurs in the electrocardiogram (ECG) signal. It is possible to classify stress signals by analyzing ECG signals and extracting specific parameters. To maximize the performance of the proposed stress classification algorithm, fast Fourier transform (FFT) and spectrograms were applied to preprocess ECG signals and produce signals in both the time and frequency domains to aid the training process. As the performance evaluation benchmarks of the stress classification model, confusion matrices, receiver operating characteristic (ROC) curves, and precision-recall (PR) curves were used, and the accuracy achieved by the proposed model was 98.3%, which is an improvement of 14.7% compared to previous research results. Therefore, our model can help manage the mental health of people exposed to stress. In addition, if combined with various biosignals such as electromyogram (EMG) and photoplethysmography (PPG), it may have the potential for development in various healthcare systems, such as home training, sleep state analysis, and cardiovascular monitoring.

The Concept of Advanced Multi-Sensor Monitoring of Human Stress

Many people live under stressful conditions which has an adverse effect on their health. Human stress, especially long-term one, can lead to a serious illness. Therefore, monitoring of human stress influence can be very useful. We can monitor stress in strictly controlled laboratory conditions, but it is time-consuming and does not capture reactions, on everyday stressors or in natural environment using wearable sensors, but with limited accuracy. Therefore, we began to analyze the current state of promising wearable stress-meters and the latest advances in the record of related physiological variables. Based on these results, we present the concept of an accurate, reliable and easier to use telemedicine device for long-term monitoring of people in a real life. In our concept, we ratify with two synchronized devices, one on the finger and the second on the chest. The results will be obtained from several physiological variables including electrodermal activity, heart rate and respiration, body temperature, blood pressure and others. All these variables will be measured using a coherent multi-sensors device. Our goal is to show possibilities and trends towards the production of new telemedicine equipment and thus, opening the door to a widespread application of human stress-meters.