Changes In Physiological Signals Research Articles

Changes in physiological signal entropy in patients with obstructive sleep apnoea: a systematic review

Background and Objective Obstructive sleep apnoea (OSA) affects an estimated 936 million people worldwide, yet only 15% receive a definitive diagnosis. Diagnosis of OSA poses challenges due to the dynamic nature of physiological signals such as oxygen saturation (SpO2) and heart rate variability (HRV). Linear analysis methods may not fully capture the irregularities present in these signals. The application of entropy of routine physiological signals offers a promising method to better measure variabilities in dynamic biological data. This review aims to explore entropy changes in physiological signals among individuals with OSA.
Methods Keyword and title searches were performed on Medline, Embase, Scopus, and CINAHL databases. Studies had to analyse physiological signals in OSA using entropy. Quality assessment used the Newcastle-Ottawa Scale. Evidence was qualitatively synthesized, considering entropy signals, entropy type, and time-series length.
Main results Twenty-two studies were included. Multiple physiological signals related to OSA, including SpO2, HRV, and the oxygen desaturation index (ODI), have been investigated using entropy. Results revealed a significant decrease in HRV entropy in those with OSA compared to control groups. Conversely, SpO2 and ODI entropy values were increased in OSA. Despite variations in entropy types, time scales, and data extraction devices, studies using receiver operating characteristic (ROC) curves demonstrated a high discriminative accuracy (>80% AUC) in distinguishing OSA patients from control groups. 
Conclusions This review highlights the potential of SpO2 entropy analysis in developing new diagnostic indices for patients with OSA. Further investigation is needed before applying this technique clinically.
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Detection of Common Respiratory Infections, Including COVID-19, Using Consumer Wearable Devices in Health Care Workers: Prospective Model Validation Study.

The early detection of respiratory infections could improve responses against outbreaks. Wearable devices can provide insights into health and well-being using longitudinal physiological signals. The purpose of this study was to prospectively evaluate the performance of a consumer wearable physiology-based respiratory infection detection algorithm in health care workers. In this study, we evaluated the performance of a previously developed system to predict the presence of COVID-19 or other upper respiratory infections. The system generates real-time alerts using physiological signals recorded from a smartwatch. Resting heart rate, respiratory rate, and heart rate variability measured during the sleeping period were used for prediction. After baseline recordings, when participants received a notification from the system, they were required to undergo testing at a Northwell Health System site. Participants were asked to self-report any positive tests during the study. The accuracy of model prediction was evaluated using respiratory infection results (laboratory results or self-reports), and postnotification surveys were used to evaluate potential confounding factors. A total of 577 participants from Northwell Health in New York were enrolled in the study between January 6, 2022, and July 20, 2022. Of these, 470 successfully completed the study, 89 did not provide sufficient physiological data to receive any prediction from the model, and 18 dropped out. Out of the 470 participants who completed the study and wore the smartwatch as required for the 16-week study duration, the algorithm generated 665 positive alerts, of which 153 (23.0%) were not acted upon to undergo testing for respiratory viruses. Across the 512 instances of positive alerts that involved a respiratory viral panel test, 63 had confirmed respiratory infection results (ie, COVID-19 or other respiratory infections detected using a polymerase chain reaction or home test) and the remaining 449 had negative upper respiratory infection test results. Across all cases, the estimated false-positive rate based on predictions per day was 2%, and the positive-predictive value ranged from 4% to 10% in this specific population, with an observed incidence rate of 198 cases per week per 100,000. Detailed examination of questionnaires filled out after receiving a positive alert revealed that physical or emotional stress events, such as intense exercise, poor sleep, stress, and excessive alcohol consumption, could cause a false-positive result. The real-time alerting system provides advance warning on respiratory viral infections as well as other physical or emotional stress events that could lead to physiological signal changes. This study showed the potential of wearables with embedded alerting systems to provide information on wellness measures.

Exploring emotional experiences and dataset construction in the era of short videos based on physiological signals

ObjectiveThis study aims to uncover the intrinsic links between emotional experiences and physiological responses as users watch short videos on social media platforms, with a particular focus on using changes in physiological signals to identify and understand different emotional states. MethodsTo achieve this objective, a simulated experiment was designed to browse short videos and induce and record physiological signals under seven typical emotional states. The recorded signals included electroencephalogram, galvanic skin response, skin temperature, and heart rate, resulting in the creation of a dataset. Machine learning algorithms were employed to classify emotions and evaluate the dataset’s effectiveness. Statistical testing methods were used to analyze signal feature changes and distributions across different emotional states, exploring trends and their statistical significance. ResultsThe study successfully constructed an emotion-physiological signal dataset. Statistical tests revealed significant changes in physiological signal characteristics across different emotional states, providing extensive data support for understanding how emotions specifically affect physiological responses. ConclusionThe research not only confirmed the practicality of the constructed dataset in emotion recognition tasks but also provided empirical evidence of how emotions influence physiological responses through detailed analysis of physiological signals. SignificanceThe findings of this study hold significant value for emotional science, psychological research, and the entertainment industry. They not only facilitate a deeper exploration of individual emotional physiological mechanisms but also provide a scientific basis for optimizing content recommendation systems, intelligent entertainment technologies, and affective-aware applications.

Ecological momentary assessments of real-world speech listening are associated with heart rate and acoustic condition

Background and aimEcological momentary assessment (EMA) can provide insights into the real-world auditory ecology of hearing aid (HA) users. To better understand what factors, influence the real-world listening experiences of this population, more detailed models of human auditory ecology and behavior are needed. Laboratory studies suggest that physiological measures are sensitive to different listening experiences, as changes in physiological signals (e.g., pupil dilation) have been associated with effortful listening. In addition, real-world heart rate (HR) has been shown to be sensitive to acoustic influences (e.g., sound pressure level, SPL, and signal-to-noise ratio, SNR). Here, we hypothesized that including physiological and acoustic data in models predicting EMA ratings can provide additional insights into real-world listening outcome. To test this, we collected and analyzed longitudinal data from individuals with normal hearing.MethodFifteen normal-hearing adults completed smartphone-based EMAs regarding their listening experiences during a 2-week period. When completing the EMAs, they had to indicate their current listening intent. The participants received a single HA each that they fastened to their collars. The HAs were used to collect continuous SPL and SNR data in the participants' daily environments. Wristbands worn by the participants were used to collect continuous HR data.ResultsLinear mixed-effects models with SPL, SNR, and HR as fixed effects and participant as random intercept showed that higher SPL and lower SNR were associated with lower (poorer) EMA ratings. Including listening intent in the analyses revealed increased HR in “speech communication” and “focused listening” situations to be associated with better EMA ratings relative to situations without any specific listening intent.ConclusionsOur findings indicate that including in-situ HR and acoustic measurements can improve the prediction of real-world listening experiences. Further, they suggest that listening intent significantly impacts self-reported listening experiences and their association with physiological responses. Specifically, better listening experiences in speech communication situations are associated with higher HR.

Measuring the Effectiveness of a Multicomponent Program to Manage Academic Stress through a Resilience to Stress Index.

In this work, we evaluate the effectiveness of a multicomponent program that includes psychoeducation in academic stress, mindfulness training, and biofeedback-assisted mindfulness, while enhancing the Resilience to Stress Index (RSI) of students through the control of autonomic recovery from psychological stress. Participants are university students enrolled in a program of excellence and are granted an academic scholarship. The dataset consists of an intentional sample of 38 undergraduate students with high academic performance, 71% (27) women, 29% (11) men, and 0% (0) non-binary, with an average age of 20 years. The group belongs to the "Leaders of Tomorrow" scholarship program from Tecnológico de Monterrey University, in Mexico. The program is structured in 16 individual sessions during an eight-week period, divided into three phases: pre-test evaluation, training program, and post-test evaluation. During the evaluation test, an assessment of the psychophysiological stress profile is performed while the participants undergo a stress test; it includes simultaneous recording of skin conductance, breathing rate, blood volume pulse, heart rate, and heart rate variability. Based on the pre-test and post-test psychophysiological variables, an RSI is computed under the assumption that changes in physiological signals due to stress can be compared against a calibration stage. The results show that approximately 66% of the participants improved their academic stress management after the multicomponent intervention program. A Welch's t-test showed a difference in mean RSI scores (t = -2.30, p = 0.025) between the pre-test and post-test phases. Our findings show that the multicomponent program promoted positive changes in the RSI and in the management of the psychophysiological responses to academic stress.

Physiological Status Prediction Based on a Novel Hybrid Intelligent Scheme.

Physiological status plays an important role in clinical diagnosis. However, the temporal physiological data change dynamically with time, and the amount of data is large; furthermore, obtaining a complete history of data has become difficult. We propose a hybrid intelligent scheme for physiological status prediction, which can be effectively utilized to predict the physiological status of patients and provide a reference for clinical diagnosis. Our proposed scheme initially extracted the attribute information of nonlinear dynamic changes in physiological signals. The maximum discriminant feature subset was selected by employing conditional relevance mutual information feature selection. An optimal subset of features was fed into the particle swarm optimization-support vector machine classifier to perform classification. For the prediction task, the proposed hybrid intelligent scheme was tested on the Sleep Heart Health Study dataset for sleep status prediction. Experimental results demonstrate that our proposed intelligent scheme outperforms the conventional machine learning classification methods.

Specific study examining silver nanoparticle-induced adaptable physiological signal changes in Phanerochaete chrysosporium in a tetracycline hydrochloride-abundant system

Numerous studies have documented the toxicity of AgNPs to cells; however, few studies have attempted to explore the intrinsic mechanistic relationships between fungal cells and contaminants, particularly in the context of changes in physiological signaling in the presence of AgNPs. Therefore, we investigated the response of the model white-rot fungal species Phanerochaete chrysosporium (P. chrysosporium) to different concentrations of AgNPs in a tetracycline hydrochloride environment. The results revealed that tetracycline hydrochloride (TCH) enhanced the detoxification of P. chrysosporium at low concentrations of silver nanoparticles (AgNPs). In the TCH-abundant system, a relatively low concentration (1.0 μM) of AgNPs activated the antioxidant stress response of P. chrysosporium, enhanced the expression of H+-ATPase, K+-ATPase, and Ca2+-ATPase, increased the flow of cellular mobility signals (H+, K+, Ca2+, and O2 fluxes), and elevated cellular activity for detoxification. Higher concentrations (50.0 μM) of AgNPs inhibited cellular responses in regard to ATPase activity and mobile signals and induced high levels of reactive oxygen species (ROS), ultimately leading to cell death. The effect of AgNPs on P. chrysosporium genes in the TCH-abundant system was also investigated, and significant expression of genes related to transmembrane transport, stress response, and substance and energy metabolism was observed. This study examining the adaptive physiological response of P. chrysosporium to AgNPs in an antibiotic environment provides a new understanding of the intrinsic interaction between this fungus and contaminants.

Abstract 14606: Accurate Detection of Acute Psychological Stress Events Using Single Lead ECG Data

Introduction: Stress has been linked to numerous health conditions, including heart disease, diabetes, and mental health issues. By monitoring changes in physiological signals, such as heart rate (HR) and heart rate variability (HRV), wearable biosensing technology allows acute stress to be non-invasively tracked over long periods, providing valuable insights for preventative healthcare. Methods: This two-phase study comprised several protocols designed to induce varying levels of psychological stress in participants (N=39). HR and HRV metrics, derived from electrocardiogram (ECG) data collected throughout the protocol on the single lead HeartKey ® Chest Module, were used by the HeartKey Stress algorithm to generate a relative stress score (0-100), which was validated against two clinically recognized methodologies for assessing patient stress: i) state-trait anxiety index (STAI), a questionnaire that subjectively measured the individual’s perceptual stress after each stage of the protocol, and ii) electrodermal activity (EDA), which continuously monitored conductive changes at the skin’s surface with an Empatica ® E4 wrist wearable. Results: Over both phases, participant STAI scores increased significantly during stress protocols (49.9 ± 23.3) relative to the baseline (30.0 ± 10.0). Mean HR showed a similar significant increase ( p < 0.001), and HRV gradually decreased throughout the testing protocol. HeartKey Stress scores derived from HR and HRV data showed a strong correlation to STAI scores. Furthermore, the HeartKey Stress trend closely replicated that of the EDA data. Conclusions: HeartKey Stress algorithm consistently generated accurate and reliable stress scores in response to events of induced, acute psychological stress. The results suggest that the algorithm has potential utility for continuous clinical monitoring of patients with stress-related illnesses.

Stability Analysis of fMRI BOLD Signals for Disease Diagnosis.

Previous studies have demonstrated that the stability changes in physiological signals can reflect individuals' pathological conditions. Apart from this, according to system science theory, a large-scale system can generally be divided into many subsystems whose stability level govern its overall performance. Therefore, this study attempts to investigate the possibility of analyzing the stability of decomposed subsystems of resting-state fMRI (rs-fMRI) BOLD signals in order to assess the overall characteristic of the human brain and individuals' health conditions. We used attention deficit/hyperactive disorder (ADHD) as an example to illustrate our method. Rs-fMRI BOLD signals were first decomposed into dynamic modes (DMs) which can illuminate the patterns of brain subsystems. Each DM is associated with one eigenvalue that determines its stability as well as oscillation frequency. Accordingly, we divided the DMs within common BOLD frequency bands into stable and unstable DMs. Then, the features related to the stability of those DMs were extracted, and nine common classifiers were used to differentiate healthy controls from ADHD patients taken from ADHD-200, a well-known dataset. The results showed that almost all features were statistically significant. Additionally, our proposed approach outperforms all existing methods with the highest possible precision, recall, and area under the receiver operating characteristic curve of 100%. In sum, we are the first to evaluate the stability of BOLD signals and demonstrate its possibility for disease diagnosis. This method can unveil new mechanisms of brain function, and could be widely used in medicine and engineering.

Smart Clothing as a Noninvasive Method to Measure the Physiological Cardiac Parameters.

In response to global aging, there have been improvements in healthcare, exercise therapy, health promotion, and other areas. There is a gradually increasing demand for such equipment for health purposes. The main purpose of smart clothing is to monitor the physical health status of the user and analyze the changes in physiological signals of the heart. Therefore, this study aimed to examine the factors that affect the measurement of the heart’s physiological parameters and the users’ comfort while wearing smart clothing as well as to validate the data obtained from smart clothing. This study examined the subjective feelings of users (aged 20–60 years) regarding smart clothing comfort (within 12 h); the median values were comfortable and above (3.4–4.5). The clothing was combined with elastic conductive fiber and spandex to decrease the relative movement of the fiber that acts as a sensor and increase the user’s comfort. Future studies should focus on the optimization of the data obtained using smart clothing. In addition to its use in medical care and post-reconstructive surgery, smart clothing can be used for home care of older adults and infants.

Physiological synchrony in EEG, electrodermal activity and heart rate reflects shared selective auditory attention

Objective. Concurrent changes in physiological signals across multiple listeners (physiological synchrony—PS), as caused by shared affective or cognitive processes, may be a suitable marker of selective attentional focus. We aimed to identify the selective attention of participants based on PS with individuals sharing attention with respect to different stimulus aspects. Approach. We determined PS in electroencephalography (EEG), electrodermal activity (EDA) and electrocardiographic inter-beat interval (IBI) of participants who all heard the exact same audio track, but were instructed to either attend to the audiobook or to interspersed auditory events such as affective sounds and beeps that attending participants needed to keep track of. Main results. PS in all three measures reflected the selective attentional focus of participants. In EEG and EDA, PS was higher for participants when linked to participants with the same attentional instructions than when linked to participants instructed to focus on different stimulus aspects, but in IBI this effect did not reach significance. Comparing PS between a participant and members from the same or the different attentional group allowed for the correct identification of the participant’s attentional instruction in 96%, 73% and 73% of the cases, for EEG, EDA and IBI, respectively, all well above chance level. PS with respect to the attentional groups also predicted performance on post-audio questions about the groups’ stimulus content. Significance. Our results show that selective attention of participants can be monitored using PS, not only in EEG, but also in EDA and IBI. These results are promising for real-world applications, where wearables measuring peripheral signals like EDA and IBI may be preferred over EEG sensors.

Automatic Detection of Target Engagement in Transcutaneous Cervical Vagal Nerve Stimulation for Traumatic Stress Triggers.

Transcutaneous cervical vagal nerve stimulation (tcVNS) devices are attractive alternatives to surgical implants, and can be applied for a number of conditions in ambulatory settings, including stress-related neuropsychiatric disorders. Transferring tcVNS technologies to at-home settings brings challenges associated with the assessment of therapy response. The ability to accurately detect whether tcVNS has been effectively delivered in a remote setting such as the home has never been investigated. We designed and conducted a study in which 12 human subjects received active tcVNS and 14 received sham stimulation in tandem with traumatic stress, and measured continuous cardiopulmonary signals including the electrocardiogram (ECG), photoplethysmogram (PPG), seismocardiogram (SCG), and respiratory effort (RSP). We extracted physiological parameters related to autonomic nervous system activity, and created a feature set from these parameters to: 1) detect active (vs. sham) tcVNS stimulation presence with machine learning methods, and 2) determine which sensing modalities and features provide the most salient markers of tcVNS-based changes in physiological signals. Heart rate (ECG), vasomotor activity (PPG), and pulse arrival time (ECG+PPG) provided sufficient information to determine target engagement (compared to sham) in addition to other combinations of sensors. resulting in 96% accuracy, precision, and recall with a receiver operator characteristics area of 0.96. Two commonly utilized sensing modalities (ECG and PPG) that are suitable for home use can provide useful information on therapy response for tcVNS. The methods presented herein could be deployed in wearable devices to quantify adherence for at-home use of tcVNS technologies.

Towards Classifying Cognitive Performance by Sensing Electrodermal Activity in Children With Specific Learning Disorders

When children suffer from cognitive disorders, school performance and social environment are affected. Measuring changes in cognitive progress is essential for assessing the clinical follow-up of the patient's cognitive abilities. This process is considered as a challenge in ambulatory settings, where follow-ups should be non-invasive and continuous. Psychophysiological measures are an objective and unobtrusive evaluation alternative for recognizing cognitive changes. This paper aims to validate the relationship between cognition and the changes in physiological signals of children suffering from Specific Learning Disorders (SLD). This validation was carried out in an eHealth rehabilitation context (with the HapHop-Physio game). Electrodermal activity (EDA) signals were collected, processed, and analyzed through a machine learning approach. Obtained results were: a dataset built from wearable physiological data and a supervised classification model. The classification model can identify the children's cognitive performance (class) from the features of the tonic component of the EDA signal (attributes) with an accuracy of 79.95%. The presented results evidence that psychophysiological measures could allow for a highly objective follow-up for patients. They can also lead to creating a basis for further improvement of rehabilitation environments and developing neurofeedback applications.

Sniffing behaviour‐related changes in cardiac and cortical activity in rats

High-frequency (HF) sniffing represents active odour sampling and an increase in the animal's motivation. We examined how HF sniffing affects the physiological activity of the brain-body system. During HF sniffing, heart rates and the ratio of theta to delta critical local field potential power were comparable to those observed during motion periods. Vagus nerve spike rates did not vary depending on HF sniffing. Our results suggest that physiological factors in the central nervous system and the periphery are not simply determined by locomotion but are crucially associated with HF sniffing. Sniffing is a fundamental behaviour for odour sampling, and high-frequency (HF) sniffing, generally at a sniff frequency of more than 6Hz, is considered to represent an animal's increased motivation to explore external environments. Here, we examined how HF sniffing is associated with changes in physiological signals from the central and peripheral organs in rats. During HF sniffing while the rats were stationary, heart rates, the magnitude of dorsal neck muscle contraction, and the ratio of theta to delta local field potential power in the motor cortex were comparable to those observed during motion periods and were significantly higher than those observed during resting respiration periods. No pronounced changes in vagus nerve spike rates were detected in relation to HF sniffing. These results demonstrate that central and peripheral physiological factors are crucially associated with the emergence of HF sniffing, especially during quiescent periods. Behavioural data might be improved to more accurately evaluate an animal's internal psychological state if they are combined with a sniffing pattern as a physiological marker.

Wearable Technology for Detecting Significant Moments in Individuals with Dementia.

The detection of significant moments can support the care of individuals with dementia by making visible what is most meaningful to them and maintaining a sense of interpersonal connection. We present a novel intelligent assistive technology (IAT) for the detection of significant moments based on patterns of physiological signal changes in individuals with dementia and their caregivers. The parameters of the IAT are tailored to each individual's idiosyncratic physiological response patterns through an iterative process of incorporating subjective feedback on videos extracted from candidate significant moments identified through the IAT algorithm. The IAT was tested on three dyads (individual with dementia and their primary caregiver) during an eight-week movement program. Upon completion of the program, the IAT identified distinct, personal characteristics of physiological responsiveness in each participant. Tailored algorithms could detect moments of significance experienced by either member of the dyad with an agreement with subjective reports of 70%. These moments were constituted by both physical and emotional significances (e.g., experiences of pain or anxiety) and interpersonal significance (e.g., moments of heighted connection). We provide a freely available MATLAB toolbox with the IAT software in hopes that the assistive technology community can benefit from and contribute to these tools for understanding the subjective experiences of individuals with dementia.

Wearables and Location Tracking Technologies for Mental-State Sensing in Outdoor Environments

Advances in commercial wearable devices are increasingly facilitating the collection and analysis of everyday physiological data. This article discusses the theoretical and practical aspects of using such ambulatory devices for the detection of episodic changes in physiological signals as a marker for mental state in outdoor environments. A pilot study was conducted to evaluate the feasibility of using commercial wearables in combination with location tracking technologies. The study measured physiological signals for fifteen participants, including heart rate, heart rate variability, and skin conductance. Participants’ signals were recorded during an outdoor walk that was tracked using a Global Positioning System logger. The walk was designed to pass through various types of environments including green, blue, and urban spaces, as well as a more stressful road crossing. The data that were obtained were used to demonstrate how biosensor information can be contextualized and enriched using location information. Significant episodic changes in physiological signals under real-world conditions were detectable in the stressful road crossing but not in the other types of environments. The article concludes that despite challenges and limitations of current off-the-shelf wearables, the utilization of these devices offers novel opportunities for evaluating episodic changes in physiological signals as a marker for mental state during everyday activities including in outdoor environments. Key Words: electrodermal activity, GPS, mental state, stress, wearable.

A Knowledge Discovery in Motion Sickness: A Comprehensive Literature Review

Motion sickness is a common perturbation experienced by humans in response to motion stimuli. The motion can happen in either real or virtual environments perceived by the vestibular system and visual illusion. The extensive varieties of research studies have been conducted in order to determine and evaluate aspects of motion sickness and its symptoms. To provide insights upon physiological changes in regards to motion sickness, researchers have used subjects from different ages, gender in addition to electrode positions and environmental conditions. The main purpose of this study is to provide a comprehensive review and comparison of the existing research studies regarding aspects of interference of the existence and augmentation of motion sickness. In this paper, we discuss the appearance of symptoms after motion sickness and summarize the physiological behaviors and emotions via a range of scenarios. In addition, the existing methods for measuring motion sickness levels are compared and discussed in detail. This study considers a number of important factors such as age, gender, health condition, participants (non/fatigue or non/drowsiness), road conditions, and different experimental set-ups impacting the results of motion sickness. Finally, this paper presents a range of practical methods to minimize and prevent the unpleasant side effects of motion sickness. This includes air ventilation, homogenized road/virtual environment features, and providing comfortable set-up and pre-movement before visual acceleration. A deeper understanding of changes in physiological signals during vection helps us to confirm the traditional subjective report and also improves our knowledge in the concept the vection.

Detection of patients with methamphetamine dependence with cue-elicited heart rate variability in a virtual social environment

In the present study, we developed a methamphetamine (METH)-related virtual social environment to elicit subjective craving and physiological reactivity. Sixty-one male patients who were abstinent from METH use and 45 age-matched healthy males (i.e., normal controls) were recruited. The physiological electrocardiogram (ECG) signals were recorded before (resting-state condition) and during viewing of a METH-cue video in the virtual environment (cue-induced condition). The cue-induced subjective craving was measured with a visual analogue scale (VAS) for patients with METH dependence. The results indicated that the cue-induced condition elicited significant differences in heart rate variability (HRV) between patients with METH dependence and normal controls. The changes of HRV indexes on time domain and non-linear domain from the resting-state condition to the cue-induced condition were positively correlated with the score on VAS of METH craving. Using a supervised machine learning algorithm with the features extracted from HRV changes, our results showed that the discriminant model provided a high predictive power for distinguishing patients with METH dependence from normal controls. Our findings support that immersing subjects with METH dependence in a METH-related virtual social environment can successfully induce physiological reactivity, and cue-induced physiological signal changes may have a potential implication in clinical practice.

Objective detection of chronic stress using physiological parameters.

The aim of this study was to design a system to diagnose chronic stress, based on blunted reactivity of the autonomic nervous system (ANS) to cognitive load (CL). The system concurrently measures CL-induced variations in pupil diameter (PD), heart rate (HR), pulse wave amplitude (PWA), galvanic skin response (GSR), and breathing rate (BR). Measurements were recorded from 58 volunteers whose stress level was identified using the State-Trait Anxiety Inventory. Number-multiplication questions were used as CLs. HR, PWA, GSR, and PD were significantly (p < 0.05) changed during CL. CL-induced changes in PWA (16.87 ± 21.39), GSR (- 13.71 ± 7.86), and PD (11.56 ± 9.85) for non-stressed subjects (n = 36) were significantly different (p < 0.05) from those in PWA (2.92 ± 12.89), GSR (- 6.87 ± 9.54), and PD (4.51 ± 10.94) for stressed subjects (n = 22). ROC analysis for PWA, GSR, and PD illustrated their usefulness to identify stressed subjects. By inputting all features to different classification algorithms, up to 91.7% of sensitivity and 89.7% of accuracy to identify stressed subjects were achieved using 10-fold cross-validation. This study was the first to document blunted CL-induced changes in PWA, GSR, and PD in stressed subjects, compared to those in non-stressed subjects. Preliminary results demonstrated the ability of our system to objectively detect chronic stress with good accuracy, suggesting the potential for monitoring stress to prevent dangerous stress-related diseases. Graphical abstract Chronic stress degrads the autonomic nervous system reaction to cognitive loads. Measurement of reduced changes in physiological signals during asking math questions was useful to identify people with high STAI score (stressed subjects).

Entropy Change of Biological Dynamics in Asthmatic Patients and Its Diagnostic Value in Individualized Treatment: A Systematic Review.

Asthma is a chronic respiratory disease featured with unpredictable flare-ups, for which continuous lung function monitoring is the key for symptoms control. To find new indices to individually classify severity and predict disease prognosis, continuous physiological data collected from monitoring devices is being studied from different perspectives. Entropy, as an analysis method for quantifying the inner irregularity of data, has been widely applied in physiological signals. However, based on our knowledge, there is no such study to summarize the complexity differences of various physiological signals in asthmatic patients. Therefore, we organized a systematic review to summarize the complexity differences of important signals in patients with asthma. We searched several medical databases and systematically reviewed existing asthma clinical trials in which entropy changes in physiological signals were studied. As a conclusion, we find that, for airflow, heart rate variability, center of pressure and respiratory impedance, their entropy values decrease significantly in asthma patients compared to those of healthy people, while, for respiratory sound and airway resistance, their entropy values increase along with the progression of asthma. Entropy of some signals, such as respiratory inter-breath interval, shows strong potential as novel indices of asthma severity. These results will give valuable guidance for the utilization of entropy in physiological signals. Furthermore, these results should promote the development of management and diagnosis of asthma using continuous monitoring data in the future.