Wearable Ring-Shaped Biomedical Device for Physiological Monitoring through Finger-Based Acquisition of Electrocardiographic, Photoplethysmographic, and Galvanic Skin Response Signals: Design and Preliminary Measurements

Wearable health devices (WHDs) are rapidly gaining ground in the biomedical field due to their ability to monitor the individual physiological state in everyday life scenarios, while providing a comfortable wear experience. This study introduces a novel wearable biomedical device capable of synchronously acquiring electrocardiographic (ECG), photoplethysmographic (PPG), galvanic skin response (GSR) and motion signals. The device has been specifically designed to be worn on a finger, enabling the acquisition of all biosignals directly on the fingertips, offering the significant advantage of being very comfortable and easy to be employed by the users. The simultaneous acquisition of different biosignals allows the extraction of important physiological indices, such as heart rate (HR) and its variability (HRV), pulse arrival time (PAT), GSR level, blood oxygenation level (SpO2), and respiratory rate, as well as motion detection, enabling the assessment of physiological states, together with the detection of potential physical and mental stress conditions. Preliminary measurements have been conducted on healthy subjects using a measurement protocol consisting of resting states (i.e., SUPINE and SIT) alternated with physiological stress conditions (i.e., STAND and WALK). Statistical analyses have been carried out among the distributions of the physiological indices extracted in time, frequency, and information domains, evaluated under different physiological conditions. The results of our analyses demonstrate the capability of the device to detect changes between rest and stress conditions, thereby encouraging its use for assessing individuals' physiological state. Furthermore, the possibility of performing synchronous acquisitions of PPG and ECG signals has allowed us to compare HRV and pulse rate variability (PRV) indices, so as to corroborate the reliability of PRV analysis under stationary physical conditions. Finally, the study confirms the already known limitations of wearable devices during physical activities, suggesting the use of algorithms for motion artifact correction.

Wearable Multisensor Ring-Shaped Probe for Assessing Stress and Blood Oxygenation: Design and Preliminary Measurements

The increasing interest in innovative solutions for health and physiological monitoring has recently fostered the development of smaller biomedical devices. These devices are capable of recording an increasingly large number of biosignals simultaneously, while maximizing the user's comfort. In this study, we have designed and realized a novel wearable multisensor ring-shaped probe that enables synchronous, real-time acquisition of photoplethysmographic (PPG) and galvanic skin response (GSR) signals. The device integrates both the PPG and GSR sensors onto a single probe that can be easily placed on the finger, thereby minimizing the device footprint and overall size. The system enables the extraction of various physiological indices, including heart rate (HR) and its variability, oxygen saturation (SpO₂), and GSR levels, as well as their dynamic changes over time, to facilitate the detection of different physiological states, e.g., rest and stress. After a preliminary SpO₂ calibration procedure, measurements have been carried out in laboratory on healthy subjects to demonstrate the feasibility of using our system to detect rapid changes in HR, skin conductance, and SpO₂ across various physiological conditions (i.e., rest, sudden stress-like situation and breath holding). The early findings encourage the use of the device in daily-life conditions for real-time monitoring of different physiological states.

Interactive slide selection algorithm and machine learning in psychophysiological memory testing

Objective. To present a new type of concealed information test (CIT) that implements the interactive slide selection (ISS) algorithm and compare its effectiveness with a standard CIT (sCIT).Approach. The ISS algorithm presents slides interactively, based on the analysis of electrodermal activity, while sCIT presents slides in a predefined, sequential order. The algorithm automatically selects irrelevant, relevant, and control slides and presents them at the moment which is physiologically most suitable for electrodermal response detection. To compare the ISS-based CIT (issCIT) and sCIT, two objects, a bag, and a wallet, were presented to 64 participants, 32 of whomwere analyzed with sCIT, and another 32 with issCIT.Main results. The results show that ISS had significantly better true/false predictions (Fisher's exact test,p< 0.01). Also, the number of false positives (FPs) was significantly lower in the issCIT group in comparison with sCIT (Fisher's exact test,p< 0.001). Machine learning (ML) classifiers improved precision from 49% to 79% in the sCIT group (McNemar's test,p< 0.05), and from 85% to 100% in the issCIT group (McNemar's test,p< 0.05). The testing time in the issCIT group ranged between 42 and 107 s, while the average was 53 s. In the sCIT group, the testing time was always 330 s.Significance. Under the presented experimental settings, the ISS algorithm obtained significantly better classification results compared to sCIT, while the application of the ML algorithms managed to improve the classification results in both groups reaching a precision of 100%. The ISS algorithm allowed for a much shorter testing time compared to sCIT.

Galvanic Skin Response Features in Psychiatry and Mental Disorders: A Narrative Review

This narrative review is aimed at presenting the galvanic skin response (GSR) Biofeedback method and possibilities for its application in persons with mental disorders as a modern form of neurorehabilitation. In the treatment of mental disorders of various backgrounds and courses, attention is focused on methods that would combine pharmacological treatment with therapies improving functioning. Currently, the focus is on neuronal mechanisms which, being physiological markers, offer opportunities for correction of existing deficits. One such indicator is electrodermal activity (EDA), providing information about emotions, cognitive processes, and behavior, and thus, about the function of various brain regions. Measurement of the galvanic skin response (GSR), both skin conductance level (SCL) and skin conductance responses (SCR), is used in diagnostics and treatment of mental disorders, and the training method itself, based on GSR Biofeedback, allows for modulation of the emotional state depending on needs occurring. Summary: It is relatively probable that neurorehabilitation based on GSR-BF is a method worth noticing, which-in the future-can represent an interesting area of rehabilitation supplementing a comprehensive treatment for people with mental disorders.

Assessing systemic vascular resistance using arteriolar pulse transit time based on multi-wavelength photoplethysmography

OBJECTIVE

Sympathetic nerve activity affects blood pressure by contracting the arteriole, which can increase systemic vascular resistance (SVR). Consequently, SVR is a key factor affecting blood pressure. However, a method for measuring SVR continuously is lacking. This paper formulated and experimentally validated a method that uses the arteriolar pulse transmit time (aPTT) to track changes in SVR.

APPROACH

multi-wavelength photoplethysmogram (PPG), electrocardiogram (ECG), and galvanic skin response (GSR) data were simultaneously gathered using a measurement system designed by this study. Blood perfusion was monitored by Laser Doppler. Least mean square (LMS) is an adaptive filtering algorithm. Our LMS-based algorithm formulated in this study was used to calculate the aPTT from the multi-wavelength PPGs. A cold stimulation experiment was conducted to verify the relationship between aPTT determined by algorithm and arteriole vasodilation. An emotinal stimulation experiment conducted, in which GSR was employed to further verify the relationship between aPTT and SVR. Twenty healthy young participants were asked to watch movie clips, which excited their sympathetic nerves. The dynamic time warping (DTW) distance is applied to evaluate between correlation of GSR and aPTT.

MAIN RESULTS

The changes in aPTT was extracted using our LMS-based method. During the recovery period after cold stimulation, aPTT decreased with the average slope of -0.19, while blood perfusion increased with the average slope of 0.72. Meanwhile, 70% participants' DTW distance's median between aPTT and GSR were significantly smaller than that between PTT and GSR during emotion stimulation.

SIGNIFICANCE

Our method uses aPTT, a continuous measurable parameter, to closely reflect SVR, as verified through experiments.

The Role of Very Low Calorie Ketogenic Diet in Sympathetic Activation through Cortisol Secretion in Male Obese Population

Adipose tissue is considered an endocrine organ, and its excess compromises the immune response and metabolism of hormones and nutrients. Furthermore, the accumulation of visceral fat helps to increase the synthesis of cortisol. The hypothalamus-pituitary-adrenal (HPA) axis is a neuroendocrine system involved in maintaining homeostasis in humans under physiological conditions and stress, and cortisol is the main hormone of the HPA axis. It is known that a stress-induced diet and cortisol reactivity to acute stress factors may be related to dietary behavior. In obesity, to reduce visceral adipose tissue, caloric restriction is a valid strategy. In light of this fact, the aim of this study was to assess the effects of a commercial dietary ketosis program for weight loss on the sympathetic nervous system and HPA axis, through evaluation of salivary cortisol and GSR levels. Thirty obese subjects were recruited and assessed before and after 8 weeks of Very Low Calorie Ketogenic Diet (VLCKD) intervention to evaluate body composition and biochemical parameters. Salivary cortisol levels and GSR significantly decreased after dietary treatment; in addition, body composition and biochemical features were ameliorated. The VLCKD had a short-term positive effect on the SNS and HPA axes regulating salivary cortisol levels. Finally, the effects of the VLCKD on the SNS and HPA axis may lead to more individualized treatment strategies that integrate obesity and stress and support the usefulness of such therapeutic interventions in promoting the reduction of the individual disease burden.

Driving Stress Detection Using Multimodal Convolutional Neural Networks with Nonlinear Representation of Short-Term Physiological Signals

Mental stress can lead to traffic accidents by reducing a driver’s concentration or increasing fatigue while driving. In recent years, demand for methods to detect drivers’ stress in advance to prevent dangerous situations increased. Thus, we propose a novel method for detecting driving stress using nonlinear representations of short-term (30 s or less) physiological signals for multimodal convolutional neural networks (CNNs). Specifically, from hand/foot galvanic skin response (HGSR, FGSR) and heart rate (HR) short-term input signals, first, we generate corresponding two-dimensional nonlinear representations called continuous recurrence plots (Cont-RPs). Second, from the Cont-RPs, we use multimodal CNNs to automatically extract FGSR, HGSR, and HR signal representative features that can effectively differentiate between stressed and relaxed states. Lastly, we concatenate the three extracted features into one integrated representation vector, which we feed to a fully connected layer to perform classification. For the evaluation, we use a public stress dataset collected from actual driving environments. Experimental results show that the proposed method demonstrates superior performance for 30-s signals, with an overall accuracy of 95.67%, an approximately 2.5–3% improvement compared with that of previous works. Additionally, for 10-s signals, the proposed method achieves 92.33% classification accuracy, which is similar to or better than the performance of other methods using long-term signals (over 100 s).

Fully Integrated, Stretchable, Wireless Skin-Conformal Bioelectronics for Continuous Stress Monitoring in Daily Life

Stress is one of the main causes that increase the risk of serious health problems. Recent wearable devices have been used to monitor stress levels via electrodermal activities on the skin. Although many biosensors provide adequate sensing performance, they still rely on uncomfortable, partially flexible systems with rigid electronics. These devices are mounted on either fingers or palms, which hinders a continuous signal monitoring. A fully-integrated, stretchable, wireless skin-conformal bioelectronic (referred to as "SKINTRONICS") is introduced here that integrates soft, multi-layered, nanomembrane sensors and electronics for continuous and portable stress monitoring in daily life. The all-in-one SKINTRONICS is ultrathin, highly soft, and lightweight, which overall offers an ergonomic and conformal lamination on the skin. Stretchable nanomembrane electrodes and a digital temperature sensor enable highly sensitive monitoring of galvanic skin response (GSR) and temperature. A set of comprehensive signal processing, computational modeling, and experimental study provides key aspects of device design, fabrication, and optimal placing location. Simultaneous comparison with two commercial stress monitors captures the enhanced performance of SKINTRONICS in long-term wearability, minimal noise, and skin compatibility. In vivo demonstration of continuous stress monitoring in daily life reveals the unique capability of the soft device as a real-world applicable stress monitor.

A Comparison of Physiological Signal Analysis Techniques and Classifiers for Automatic Emotional Evaluation of Audiovisual Contents

This work focuses on finding the most discriminatory or representative features that allow to classify commercials according to negative, neutral and positive effectiveness based on the Ace Score index. For this purpose, an experiment involving forty-seven participants was carried out. In this experiment electroencephalography (EEG), electrocardiography (ECG), Galvanic Skin Response (GSR) and respiration data were acquired while subjects were watching a 30-min audiovisual content. This content was composed by a submarine documentary and nine commercials (one of them the ad under evaluation). After the signal pre-processing, four sets of features were extracted from the physiological signals using different state-of-the-art metrics. These features computed in time and frequency domains are the inputs to several basic and advanced classifiers. An average of 89.76% of the instances was correctly classified according to the Ace Score index. The best results were obtained by a classifier consisting of a combination between AdaBoost and Random Forest with automatic selection of features. The selected features were those extracted from GSR and HRV signals. These results are promising in the audiovisual content evaluation field by means of physiological signal processing.

Ivane Tarkhnishvili (Ivan Tarchanoff) and His Links With Poland

According to some sources, Professor Ivan Tarchanoff (Ivane Tarkhanishvili) died in Saint Petersburg in 1908. In fact, he died in Poland in his house in Nawojowa Góra, a suburb of Krzeszowice, not far from Kraków. A student and later assistant of Tarchanoff in Saint Petersburg, Napoleon Cybulski, was then professor of physiology at the Jagiellonian University in Kraków and its rector and deputy rector. It is hardly known that Tarchanoff spent the last three years of his life mainly in Galicia, at that time part of the Austro-Hungarian Empire. There, near Kraków, he built himself a house and it was in Galicia (in Kraków and Lemberg [Polish Lwów, today Lviv]) that he worked on some of his last papers. The strong link between Tarchanoff and Polish physiology is not sufficiently well known and deserves to be recognized.

Express-evaluation of the psycho-physiological condition of Paralympic athletes

Objective

Evaluation of elite athletes’ psycho-physiological condition at various stages of preparation and in international competition.

Design

Athletes were tested during training and participation in international competition using methods of galvanic skin response (GSR) and gas discharge visualization (GDV).

Setting

Saint Petersburg Federal Research Institute of Physical Culture and Sport, Russia and Paralympic athletic training camp, Norway.

Participants

Eighteen athletes from Russia’s Skiing and Biathlon Paralympic Team. All athletes had some level of damage to their musculoskeletal system.

Main outcome measures

Stress level (SL), energy potential (EP), and psycho-emotional tension (PET).

Results

It was found that the higher the level of EP achieved by the athlete in the training period, the lower the SL in the competition time. The SL of an athlete recorded in the training period significantly correlates with the SL both before and at the time of competition. The PET and SL before the World Cup was negatively correlated to the results of skiing competitions.

Conclusion

Evaluation of PET, EP, and SL through GSR and GDV offers a fast, highly precise, non-invasive method to assess an athlete’s level of readiness during both training and at the time of competition.