Ultra short term analysis of heart rate variability for monitoring mental stress in mobile settings

Recurrent probabilistic neural network-based short-term prediction for acute hypotension and ventricular fibrillation

In this paper, we propose a novel method for predicting acute clinical deterioration triggered by hypotension, ventricular fibrillation, and an undiagnosed multiple disease condition using biological signals, such as heart rate, RR interval, and blood pressure. Efforts trying to predict such acute clinical deterioration events have received much attention from researchers lately, but most of them are targeted to a single symptom. The distinctive feature of the proposed method is that the occurrence of the event is manifested as a probability by applying a recurrent probabilistic neural network, which is embedded with a hidden Markov model and a Gaussian mixture model. Additionally, its machine learning scheme allows it to learn from the sample data and apply it to a wide range of symptoms. The performance of the proposed method was tested using a dataset provided by Physionet and the University of Tokyo Hospital. The results show that the proposed method has a prediction accuracy of 92.5% for patients with acute hypotension and can predict the occurrence of ventricular fibrillation 5 min before it occurs with an accuracy of 82.5%. In addition, a multiple disease condition can be predicted 7 min before they occur, with an accuracy of over 90%.

Heart Rate Variability as an Index of Differential Brain Dynamics at Rest and After Acute Stress Induction

The brain continuously receives input from the internal and external environment. Using this information, the brain exerts its influence on both itself and the body to facilitate an appropriate response. The dynamic interplay between the brain and the heart and how external conditions modulate this relationship deserves attention. In high-stress situations, synchrony between various brain regions such as the prefrontal cortex and the heart may alter. This flexibility is believed to facilitate transitions between functional states related to cognitive, emotional, and especially autonomic activity. This study examined the dynamic temporal functional association of heart rate variability (HRV) with the interaction between three main canonical brain networks in 38 healthy male subjects at rest and directly after a psychosocial stress task. A sliding window approach was used to estimate the functional connectivity (FC) among the salience network (SN), central executive network (CEN), and default mode network (DMN) in 60-s windows on time series of blood-oxygen-level dependent (BOLD) signal. FC between brain networks was calculated by Pearson correlation. A multilevel linear mixed model was conducted to examine the window-by-window association between the root mean square of successive differences between normal heartbeats (RMSSD) and FC of network-pairs across sessions. Our findings showed that the minute-by-minute correlation between the FC and RMSSD was significantly stronger between DMN and CEN than for SN and CEN in the baseline session [b = 4.36, t(5025) = 3.20, p = 0.006]. Additionally, this differential relationship between network pairs and RMSSD disappeared after the stress task; FC between DMN and CEN showed a weaker correlation with RMSSD in comparison to baseline [b = −3.35, t(5025) = −3.47, p = 0.006]. These results suggest a dynamic functional interplay between HRV and the functional association between brain networks that varies depending on the needs created by changing conditions.

Nonintrusive Monitoring of Mental Fatigue Status Using Epidermal Electronic Systems and Machine-Learning Algorithms

Mental fatigue, characterized by subjective feelings of "tiredness" and "lack of energy", can degrade individual performance in a variety of situations, for example, in motor vehicle driving or while performing surgery. Thus, a method for nonintrusive monitoring of mental fatigue status is urgently needed. Recent research shows that physiological signal-based fatigue-classification methods using wearable electronics can be sufficiently accurate; by contrast, rigid, bulky devices constrain the behavior of those wearing them, potentially interfering with test signals. Recently, wearable electronics, such as epidermal electronics systems (EES) and electronic tattoos (E-tattoos), have been developed to meet the requirements for the comfortable measurement of various physiological signals. However, comfortable, effective, and nonintrusive monitoring of mental fatigue levels remains to be fulfilled. In this work, an EES is established to simultaneously detect multiple physiological signals in a comfortable and nonintrusive way. Machine-learning algorithms are employed to determine the mental fatigue levels and a predictive accuracy of up to 89% is achieved based on six different kinds of physiological features using decision tree algorithms. Furthermore, EES with the trained predictive model are applied to monitor in situ human mental fatigue levels when doing several routine research jobs, as well as the effect of relaxation methods in relieving fatigue.

A low-complexity PPG pulse detection method for accurate estimation of the pulse rate variability (PRV) during sudden decreases in the signal amplitude

BACKGROUND

One of the biggest obstacles to reliable pulse rate variability (PRV) analysis is the erroneous detection of photoplethysmographic (PPG) pulses. Among all the disturbances that may hinder pulse detection, the ripples appearing at the smooth segments of the PPG signal can become a serious problem when the amplitude of the signal decreases considerably.

OBJECTIVE

To present a low-complexity PPG pulse detection method for reliable PRV estimation under conditions in which a sudden decrease in the amplitude of the PPG signal can be expected.

APPROACH

2-min ECG and PPG data (sampling rate at 500 Hz) were obtained from thirty healthy subjects, who were asked to take a deep inspiration to provoke a sudden amplitude decrease (SAD) of the PPG signal. After introducing a new parameter denoted as C, through which it is possible to jump over the ripples hindering the accurate detection of the systolic peaks, 500 Hz-sampled PPG recordings were down-sampled (400, 300, 200 and 100 Hz) to investigate the effect of the sampling rate on pulse detection. For ECG recordings, automatic R-peak detection was performed by the Pan and Tompkins (PT) algorithm, whereas PPG pulse detection was performed by the well-known maximum of the first derivative (M1D) and the proposed method, once the C-value for best detection results on 500 Hz-sampled PPG recordings was found. The agreement between heart rate variability (HRV) and PRVs estimated from each pulse detection method was assessed and the correlation between HRV and PRV-derived indexes was computed for comparison.

MAIN RESULTS

The proposed method can perform well on PPG-SAD segments, provided that the proper value of the parameter C is used. Moreover, a good agreement between HRV and PRV series, as well as lower relative errors and higher correlation coefficients between HRV and PRV indexes, were achieved by the proposed pulse detection method during SADs.

SIGNIFICANCE

Results show that the proposed method can dynamically adapt to circumstances in which a decrease in the amplitude of the PPG signal can be expected, providing continuous systolic peak detection and reliable PRV estimation under those conditions. However, more extensive testing under a wide range of conditions is needed to perform a more rigorous validation.

Results of exploratory investigation into adherence to auditory coping instructions during an acutely stressful task

Healthcare providers often perform under significant stress, during which their performance must be optimal, but is known to suffer. Stress management interventions in this context can provide cognitive support to rescue performance. This exploratory study sought to evaluate the effect of stress intervention components on stress and performance while clinicians engaged in two versions of a computer-based task, differing in overall level of demand: one high-stress and one low-stress. Participants (N = 45) were assigned to one of five groups (N = 9 per group), where they each completed both versions of the task, under different conditions of cognitive support. Group 1 received no intervention; Group 2 received biofeedback; and Group 3 received biofeedback and explicit coping instructions. Group 4 received emotional intelligence training, and Group 5 received emotional intelligence training and biofeedback. We hypothesized that Group 3 participants would present the lowest self-reported and physiological measures of stress, and the highest performance. Results reveal that the high-stress task induced significantly higher self-reported and physiological stress/anxiety, and lower task performance. No significant main effects of experimental condition or interaction effects were detected, indicating that intervention components had minimal effect on stress and performance. However, ultra-short term physiological analysis, analyzing <5 min of HRV data, revealed significantly decreased stress (SDNN, the standard deviation of normal-to-normal peaks) following auditory coping instructions. Exploratory study results suggest that although cognitive intervention components had minimal effect on stress and performance, physiological stress may be significantly reduced immediately following adherence to a coping instruction intervention. Future work is needed.Lay summaryThis exploratory study evaluated the potential benefit of providing healthcare practitioners with various stress management components during an acutely stressful task. Our results support the positive effect of following behavioral coping instructions on immediate physiological measures of stress.

Unconstrained Vital Sign Monitoring System Using an Aortic Pulse Wave Sensor

This paper proposes a novel unconstrained monitoring system that measures heart and respiratory rates and evaluates autonomic nervous activity based on heart rate variability. The proposed system measures the aortic pulse waves (APWs) of a patient via an APW sensor that comprises a single microphone integrated into a mattress. Vital signs (i.e., heart rate, respiratory rate) and autonomic nervous activity were analyzed using the measured APWs. In an experiment with supine and seated participants, vital signs calculated by the proposed system were compared with vital signs measured with commercial devices, and we obtained the correlations of r > 0.8 for the heart rates, r > 0.7 for the respiratory rates, and r > 0.8 for the heart rate variability indices. These results indicate that the proposed system can produce accurate vital sign measurements. In addition, we performed the experiment of image stimulus presentation and explored the relationships between the self-reported psychological states evoked by the stimulus and the measured vital signs. The results indicated that vital signs reflect psychological states. In conclusion, the proposed system demonstrated its ability to monitor health conditions by actions as simple as sitting or lying on the APW sensor.

Age-related reductions in heart rate variability do not worsen during exposure to humid compared to dry heat: A secondary analysis

We conducted a secondary analysis to investigate whether age-related attenuations in heart rate variability (HRV) worsen during exposure to moderate, dry (36.5°C, 20% RH) or humid (36.5°C, 60% RH) heat conditions that resulted in greater body heat storage among older compared to young participants, and during humid compared to dry heat, regardless of age. Six HRV indices [heart rate (HR), coefficient of variation (CoV), detrended fluctuation analysis: α1, low frequency power, high frequency power, and low/high frequency ratio] were assessed in 10 young (21 ± 3 y) and 9 older (65 ± 5 y) adults for 15-min prior to (baseline), and at the end of a 120-min exposure to dry and humid heat while seated at rest. Our results demonstrated a condition (dry and humid) x time (baseline and end) interaction effect on HR (p = 0.047) such that HR gradually increased during humid heat exposure yet remained similar during dry heat exposure across groups. We also found an age-related attenuation in CoV at baseline for both the dry (young: 0.097 ± 0.023%; older: 0.054 ± 0.016%) and humid (young: 0.093 ± 0.034%; older: 0.056 ± 0.014%) heat conditions (p < 0.02). Those age-related attenuations in CoV, however, were not magnified throughout the exposure nor different between conditions (p > 0.05). While older adults stored more heat during a brief 120-min exposure to dry heat compared to their young counterparts, this was not paralleled by further age-related impairments in HRV even when body heat storage and cardiovascular strain were exacerbated by exposure to humid heat.

Effects of the Size of Mixed-Reality Person Representations on Stress and Presence in Telecommunication

We study how mixed reality (MR) telepresence can enhance long-distance human interaction and how altering 3D representations of a remote person can be used to modulate stress and anxiety during social interactions. To do so, we developed an MR telepresence system employing commodity depth sensors and Microsoft’s Hololens. A textured, polygonal 3D model of a person was reconstructed in real time and transmitted over network for rendering in remote location using HoloLens. In this study, we used mock job interview paradigm to induce stress in human–subjects interacting with an interviewer presented as an MR hologram. Participants were exposed to three different types of real-time reconstructed virtual holograms of the interviewer, a natural-sized 3D reconstruction (NR), a miniature 3D reconstruction (SR) and a 2D-display representation (LCD). Participants reported their subjective experience through questionnaires, while their biophysical responses were recorded. We found that the size of 3D representation of a remote interviewer had a significant effect on participants’ stress levels and their sense of presence. The questionnaire data showed that NR condition induced more stress and presence than SR condition and was significantly different from LCD condition. We also found consistent patterns in the biophysical data.

Effect of Landscape Microclimates on Thermal Comfort and Physiological Wellbeing

Global climate change and intensifying heat islands have reduced human thermal comfort and health in urban outdoor environments. However, there has been little research that has focused on how microclimates affect human thermal comfort, both psychologically and physiologically. We investigated the effect of a range of landscape microclimates on human thermal comfort and health using questionnaires and physiological measurements, including skin temperature, skin conductance, and heart rate variability, and compared the results with the effect of prevailing climate conditions in open spaces. We observed that in landscape microclimates, thermal sensation votes significantly decreased from 1.18 ± 0.66 (warm–hot) to 0.23 ± 0.61 (neutral–slightly warm), and thermal comfort increased from 1.18 ± 0.66 (uncomfortable–neutral) to 0.23 ± 0.61 (neutral–comfortable). In the landscape microclimates, skin temperature and skin conductance decreased 0.3 ± 0.8 °C and 0.6 ± 1.0 μs, respectively, while in the control, these two parameters increased by 0.5 ± 0.9 °C and 0.2 ± 0.7 μs, respectively. Further, in landscape microclimates, subject heart rate variability increased significantly. These results suggest landscape microclimates improve human thermal comfort and health, both psychologically and physiologically. These findings can provide an evidence base that will assist urban planners in designing urban environments for the health and wellbeing of residents.

Assessment of Ultra-Short Heart Variability Indices Derived by Smartphone Accelerometers for Stress Detection

Body acceleration due to heartbeat-induced reaction forces can be measured as mobile phone accelerometer (m-ACC) signals. Our aim was to test the feasibility of using m-ACC to detect changes induced by stress by ultra-short heart rate variability (USV) indices (standard deviation of normal-to-normal interval—SDNN and root mean square of successive differences—RMSSD). Sixteen healthy volunteers were recruited; m-ACC was recorded while in supine position, during spontaneous breathing at rest conditions (REST) and during one minute of mental stress (MS) induced by arithmetic serial subtraction task, simultaneous with conventional electrocardiogram (ECG). Beat occurrences were extracted from both ECG and m-ACC and used to compute USV indices using 60, 30 and 10 s durations, both for REST and MS. A feasibility of 93.8% in the beat-to-beat m-ACC heart rate series extraction was reached. In both ECG and m-ACC series, compared to REST, in MS the mean beat duration was reduced by 15% and RMSSD decreased by 38%. These results show that short term recordings (up to 10 s) of cardiac activity using smartphone’s accelerometers are able to capture the decrease in parasympathetic tone, in agreement with the induced stimulus.

Is There an Optimal Autonomic State for Enhanced Flow and Executive Task Performance?

Introduction

Flow describes a state of optimal experience that can promote a positive adaptation to increasing stress. The aim of the current study is to identify the ideal autonomic state for peak cognitive performance by correlating sympathovagal balance during cognitive stress with (1) perceived flow immersion and (2) executive task performance.

Materials and Methods

Autonomic states were varied in healthy male participants (n = 48) using combinations of patterned breathing and skeletal muscle contraction that are known to induce differing levels of autonomic response. After autonomic variation, a Stroop test was performed on participants to induce a mild stress response, and autonomic arousal was assessed using heart rate variability. Subjective experience of flow was measured by standardized self-report, and executive task performance was measured by reaction time on the Stroop test.

Results

There were significant associations between autonomic state and flow engagement with an inverted U-shaped function for parasympathetic stimulation, sympathetic response, and overall sympathovagal balance. There were also significant associations between autonomic states and reaction times. Combining sympathetic and parasympathetic responses to evaluate overall sympathovagal balance, there was a significant U-shaped relationship with reaction time.

Discussion

Our results support the flow theory of human performance in which the ideal autonomic state lies at the peak of an inverted-U function, and extremes at either end lead to both suboptimal flow experience. Similarly, cognitive task performance was maximized at the bottom of the U-function. Our findings suggest that optimal performance may be associated with predominant, but not total, sympathetic response.

Psychological and physiological effects of emotion focused training for self-compassion and self-protection

Emotion Focused Training for Self-Compassion and Self-Protection (EFT-SCP) is a novel intervention developed on the basis of the latest findings on self-criticism from Emotion-focused therapy and existing programs designed to cultivate compassion. EFTSCP is designed to encourage participants to cultivate self-compassion and protective anger as a way of reducing selfcriticism. Our goal was to investigate the effect of this group-based intervention on self-criticism, self-protection, and self-compassion. A total of 73 students were assigned to the EFT-SCP intervention (n=19), no-treatment control (n=34) or to an active control group (n=20). The intervention group met weekly for 1.5 hours and were instructed to incorporate EFT-SCP tasks into their daily life for 12 weeks. Whilst the no-treatment group did not undergo an intervention, the active control group completed an adapted expressive writing task once a week. In addition to the assessment of heart rate variability during imagery tasks, participants also completed self-reported measures of self-compassion and self-criticism before and after the intervention. Compared with both control groups, the intervention group showed a significant increase in heart rate variability following EFT-SCP (during self-critical imagery, P=.049; probability of superiority was .63, and during self-compassionate imagery P=.007; probability of superiority was .62, both effect sizes were medium) and significant decreases in selfcriticism (Hated Self P=.017; .34 and Inadequate Self P<.001; .33) and self-uncompassionate responding (P<.001; .39). All three effect sizes were small. Participating in EFT-SCP had a positive effect on psychological and physiological outcomes.

Heart Rate Variability reveals the fight between racially biased and politically correct behaviour

In this study, we explored vagally-mediated heart rate variability (vmHRV) responses, a psychophysiological index of cognitive self-regulatory control, to map the dynamics associated with empathic responses for pain towards an out-group member. Accordingly, Caucasian participants were asked to judge the experience of African and Caucasian actors touched with either a neutral or a harmful stimulus. Results showed that (1) explicit judgment of pain intensity in African actors yielded higher rating score and (2) took longer time compared to Caucasian actors, (3) these behavioural outcomes were associated with a significant increment of RMSSD, Log-HF-HRV and HF-HRV n.u., (4) resting HF-HRV n.u. predicted the participants’ lag-time to judge painful stimulations delivered to African actors. Interestingly, these dynamics were associated with a measure of implicit racial attitudes and were, in part, abolished when participants performed a concurrent task during videos presentation. Taken together our results support the idea that a cognitive effort is needed to self-regulate our implicit attitude as predicted by the ‘Contrasting Forces Model’.

Cardiorespiratory Fitness Predicts Greater Vagal Autonomic Activity in Drug Users Under Stress

While drug use has been shown to impair cardiac autonomic regulation, exercise might overcome some of the damage. Herein, we describe how individuals with substance use disorder (SUD) have their heart rate variability (HRV) and drug-related behaviors negatively affected in response to a stressor. However, we show how cardiorespiratory fitness may attenuate those impairments in autonomic control. Fifteen individuals with SUD were matched with 15 non-SUD individuals by age, weight, height, and fitness level, and had their HRV responses under stress induced by the Cold Pressor Test (CPT). The SUD group had lower mean of R-R intervals before and after the CPT when compared with the non-SUD group. In addition, in individuals with SUD, higher cardiorespiratory fitness level predicted greater vagal activity before, during, and after CPT. Moreover, for individuals with SUD, days of abstinence predicted greater mean of R-R intervals during recovery from the CPT. Finally, years of drug use negatively predicted mean of R-R intervals during recovery. Thus, our results suggest that chronic drug use impairs cardiac autonomic regulation at rest and after a physical stress. However, cardiorespiratory fitness might attenuate these impairments by increasing vagal autonomic activity.

Deep ECG-Respiration Network (DeepER Net) for Recognizing Mental Stress

Unmanaged long-term mental stress in the workplace can lead to serious health problems and reduced productivity. To prevent this, it is important to recognize and relieve mental stress in a timely manner. Here, we propose a novel stress detection algorithm based on end-to-end deep learning using multiple physiological signals, such as electrocardiogram (ECG) and respiration (RESP) signal. To mimic workplace stress in our experiments, we used Stroop and math tasks as stressors, with each stressor being followed by a relaxation task. Herein, we recruited 18 subjects and measured both ECG and RESP signals using Zephyr BioHarness 3.0. After five-fold cross validation, the proposed network performed well, with an average accuracy of 83.9%, an average F1 score of 0.81, and an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.92, demonstrating its superiority over conventional machine learning models. Furthermore, by visualizing the activation of the trained network’s neurons, we found that they were activated by specific ECG and RESP patterns. In conclusion, we successfully validated the feasibility of end-to-end deep learning using multiple physiological signals for recognition of mental stress in the workplace. We believe that this is a promising approach that will help to improve the quality of life of people suffering from long-term work-related mental stress.

Optimal fiducial points for pulse rate variability analysis from forehead and finger photoplethysmographic signals

OBJECTIVE

The aim of this work is to evaluate and compare five fiducial points for the temporal location of each pulse wave from forehead and finger photoplethysmographic (PPG) pulse wave signals to perform pulse rate variability (PRV) analysis as a surrogate for heart rate variability (HRV) analysis.

APPROACH

Forehead and finger PPG signals were recorded during a tilt-table test simultaneously with the electrocardiogram (ECG). Artefacts were detected and removed and five fiducial points were computed: apex, middle-amplitude and foot points of the PPG signal, apex point of the first derivative signal and the intersection point of the tangent to the PPG waveform at the apex of the derivative PPG signal and the tangent to the foot of the PPG pulse, defined as the intersecting tangents method. Pulse period (PP) time interval series were obtained from both PPG signals and compared with the RR intervals obtained from the ECG. HRV and PRV signals were estimated and classical time and frequency domain indices were computed.

MAIN RESULTS

The middle-amplitude point of the PPG signal (n _M ), the apex point of the first derivative ([Formula: see text]), and the tangent intersection point (n _T ) are the most suitable fiducial points for PRV analysis, resulting in the lowest relative errors estimated between PRV and HRV indices and higher correlation coefficients and reliability indices. Statistically significant differences according to the Wilcoxon test between PRV and HRV signals were found for the apex and foot fiducial points of the PPG, as well as the lowest agreement between RR and PP series according to Bland-Altman analysis. Hence, these signals have been considered less accurate for variability analysis. In addition, the relative errors are significantly lower for n _M and [Formula: see text] using Friedman statistics with a Bonferroni multiple-comparison test, and we propose that n _M is the most accurate fiducial point. Based on our results, forehead PPG seems to provide more reliable information for a PRV assessment than finger PPG.

SIGNIFICANCE

The accuracy of the pulse wave detection depends on the morphology of the PPG. There is therefore a need to widely define the most accurate fiducial point for performing a PRV analysis under non-stationary conditions based on different PPG sensor locations and signal acquisition techniques.

The Current State of Mobile Phone Apps for Monitoring Heart Rate, Heart Rate Variability, and Atrial Fibrillation: Narrative Review

Background

Mobile phone apps capable of monitoring arrhythmias and heart rate (HR) are increasingly used for screening, diagnosis, and monitoring of HR and rhythm disorders such as atrial fibrillation (AF). These apps involve either the use of (1) photoplethysmographic recording or (2) a handheld external electrocardiographic recording device attached to the mobile phone or wristband.

Objective

This review seeks to explore the current state of mobile phone apps in cardiac rhythmology while highlighting shortcomings for further research.

Methods

We conducted a narrative review of the use of mobile phone devices by searching PubMed and EMBASE from their inception to October 2018. Potentially relevant papers were then compared against a checklist for relevance and reviewed independently for inclusion, with focus on 4 allocated topics of (1) mobile phone monitoring, (2) AF, (3) HR, and (4) HR variability (HRV).

Results

The findings of this narrative review suggest that there is a role for mobile phone apps in the diagnosis, monitoring, and screening for arrhythmias and HR. Photoplethysmography and handheld electrocardiograph recorders are the 2 main techniques adopted in monitoring HR, HRV, and AF.

Conclusions

A number of studies have demonstrated high accuracy of a number of different mobile devices for the detection of AF. However, further studies are warranted to validate their use for large scale AF screening.

Ultra-short term HRV features as surrogates of short term HRV: a case study on mental stress detection in real life

Background

This paper suggests a method to assess the extent to which ultra-short Heart Rate Variability (HRV) features (less than 5 min) can be considered as valid surrogates of short HRV features (nominally 5 min). Short term HRV analysis has been widely investigated for mental stress assessment, whereas the validity of ultra-short HRV features remains unclear. Therefore, this study proposes a method to explore the extent to which HRV excerpts can be shortened without losing their ability to automatically detect mental stress.

Methods

ECGs were acquired from 42 healthy subjects during a university examination and resting condition. 23 features were extracted from HRV excerpts of different lengths (i.e., 30 s, 1 min, 2 min, 3 min, and 5 min). Significant differences between rest and stress phases were investigated using non-parametric statistical tests at different time-scales. Features extracted from each ultra-short length were compared with the standard short HRV features, assumed as the benchmark, via Spearman’s rank correlation analysis and Bland-Altman plots during rest and stress phases. Using data-driven machine learning approaches, a model aiming to detect mental stress was trained, validated and tested using short HRV features, and assessed on the ultra-short HRV features.

Results

Six out of 23 ultra-short HRV features (MeanNN, StdNN, MeanHR, StdHR, HF, and SD2) displayed consistency across all of the excerpt lengths (i.e., from 5 to 1 min) and 3 out of those 6 ultra-short HRV features (MeanNN, StdHR, and HF) achieved good performance (accuracy above 88%) when employed in a well-dimensioned automatic classifier.

Conclusion

This study concluded that 6 ultra-short HRV features are valid surrogates of short HRV features for mental stress investigation.

Electronic supplementary material

The online version of this article (10.1186/s12911-019-0742-y) contains supplementary material, which is available to authorized users.

Physiological Responses of the Youth Viewing a Japanese Garden

Previous studies have demonstrated that exposure to a Japanese garden is a non-pharmacological measure to improve the behavioral symptoms of elderly people with dementia, and that Japanese gardens are significantly more effective than other environments. However, it is not clear whether Japanese gardens have similar effects in the young. To address this open question, we measured the physiological responses of university students when viewing a Japanese garden, and compared them to the same students' responses when viewing a control space. We measured three physiological indicators of autonomous nervous system (ANS) activity: the electrocardiograph (ECG), the blood volume pulse (BVP) and the galvanic skin response (GSR). Our results suggest that the Japanese garden does not have as calming an effect on younger subjects as observed previously in elderly subjects. However, students did respond more positively to the Japanese garden than to an unstructured space. Ambient temperature was found to be a critical factor affecting heart rate and heart rate variability, but not other measures.

Uncertainty in heart rate complexity metrics caused by R-peak perturbations

Heart rate complexity (HRC) is a proven metric for gaining insight into human stress and physiological deterioration. To calculate HRC, the detection of the exact instance of when the heart beats, the R-peak, is necessary. Electrocardiogram (ECG) signals can often be corrupted by environmental noise (e.g., from electromagnetic interference, movement artifacts), which can potentially alter the HRC measurement, producing erroneous inputs which feed into decision support models. Current literature has only investigated how HRC is affected by noise when R-peak detection errors occur (false positives and false negatives). However, the numerical methods used to calculate HRC are also sensitive to the specific location of the fiducial point of the R-peak. This raises many questions regarding how this fiducial point is altered by noise, the resulting impact on the measured HRC, and how we can account for noisy HRC measures as inputs into our decision models. This work uses Monte Carlo simulations to systematically add white and pink noise at different permutations of signal-to-noise ratios (SNRs), time segments, sampling rates, and HRC measurements to characterize the influence of noise on the HRC measure by altering the fiducial point of the R-peak. Using the generated information from these simulations provides improved decision processes for system design which address key concerns such as permutation entropy being a more precise, reliable, less biased, and more sensitive measurement for HRC than sample and approximate entropy.

Effects of Cold Stimulation on Cardiac-Vagal Activation in Healthy Participants: Randomized Controlled Trial

Background

The experience of psychological stress has not yet been adequately tackled with digital technology by catering to healthy individuals who wish to reduce their acute stress levels. For the design of digitally mediated solutions, physiological mechanisms need to be investigated that have the potential to induce relaxation with the help of technology. Research has shown that physiological mechanisms embodied in the face and neck regions are effective for diminishing stress-related symptoms. Our study expands on these areas with the design for a wearable in mind. As this study charts new territory in research, it also is a first evaluation of the viability for a wearables concept to reduce stress.

Objective

The objectives of this study were to assess whether (1) heart rate variability would increase and (2) heart rate would decrease during cold stimulation using a thermode device compared with a (nonstimulated) control condition. We expected effects in particular in the neck and cheek regions and less in the forearm area.

Methods

The study was a fully randomized, within-participant design. Volunteer participants were seated in a laboratory chair and tested with cold stimulation on the right side of the body. A thermode was placed on the neck, cheek, and forearm. We recorded and subsequently analyzed participants’ electrocardiogram. The cold stimulation was applied in 16-second intervals over 4 trials per testing location. The control condition proceeded exactly like the cold condition, except we manipulated the temperature variable to remain at the baseline temperature. We measured heart rate as interbeat intervals in milliseconds and analyzed root mean square of successive differences to index heart rate variability. We analyzed data using a repeated-measures ANOVA (analysis of variance) approach with 2 repeated-measures factors: body location (neck, cheek, forearm) and condition (cold, control).

Results

Data analysis of 61 participants (after exclusion of outliers) showed a main effect and an interaction effect for body location and for condition, for both heart rate and heart rate variability. The results demonstrate a pattern of cardiovascular reactivity to cold stimulation, suggesting an increase in cardiac-vagal activation. The effect was significant for cold stimulation in the lateral neck area.

Conclusions

The results confirmed our main hypothesis that cold stimulation at the lateral neck region would result in higher heart rate variability and lower heart rate than in the control condition. This sets the stage for further investigations of stress reduction potential in the neck region by developing a wearable prototype that can be used for cold application. Future studies should include a stress condition, test for a range of temperatures and durations, and collect self-report data on perceived stress levels to advance findings.

Changes in MIDAS, Perceived Stress, Frontalis Muscle Activity and Non-Steroidal Anti-Inflammatory Drugs Usage in Patients with Migraine Headache without Aura following Ayurveda and Yoga Compared to Controls: An Open Labeled Non-Randomized Study

Background

There has been a significant increase in the use of complementary and integrative medicine to provide long-term healing solutions in migraine headache patients. Knowing the limitations of conventional medical approach, the present study evaluated the influence of two Indian traditional systems of medicine on migraine-related disability, autonomic variables, perceived stress, and muscle activity in patients with migraine headache without aura.

Methods

Thirty subjects recruited to the Ayurveda and Yoga (AY) group underwent traditional Panchakarma (Bio-purification) using therapeutic Purgation followed by yoga therapy, while 30 subjects of control (CT) group continued on symptomatic treatment (non-steroidal anti-inflammatory drugs [NSAID's]) for 90 days. Migraine disability assessment score, perceived stress, heart rate variability (HRV), and surface electromyography (EMG) of frontalis muscle were measured on day 1, day 30, and day 90 in both groups.

Results

Significant reduction in migraine disability and perceived stress scores were observed in the AY group. The low-frequency component of the HRV decreased significantly, the high-frequency component increased and their ratio showed improved sympathovagal balance. The EMG showed decreased activity of the frontalis muscle in the AY group compared to the control group.

Conclusion

The integrative approach combining Ayurveda and Yoga therapy reduces migraine-related disability, perceived stress, sympathetic arousal, and muscle tension.

Ultra-short heart rate variability recording reliability: The effect of controlled paced breathing

BACKGROUND

Recent studies have reported that Heart Rate Variability (HRV) indices remain reliable even during recordings shorter than 5 min, suggesting the ultra-short recording method as a valuable tool for autonomic assessment. However, the minimum time-epoch to obtain a reliable record for all HRV domains (time, frequency, and Poincare geometric measures), as well as the effect of respiratory rate on the reliability of these indices remains unknown.

METHODS

Twenty volunteers had their HRV recorded in a seated position during spontaneous and controlled respiratory rhythms. HRV intervals with 1, 2, and 3 min were correlated with the gold standard period (6-min duration) and the mean values of all indices were compared in the two respiratory rhythm conditions.

RESULTS

rMSSD and SD1 were more reliable for recordings with ultra-short duration at all time intervals (r values from 0.764 to 0.950, p < 0.05) for spontaneous breathing condition, whereas the other indices require longer recording time to obtain reliable values. The controlled breathing rhythm evokes stronger r values for time domain indices (r values from 0.83 to 0.99, p < 0.05 for rMSSD), but impairs the mean values replicability of domains across most time intervals. Although the use of standardized breathing increases the correlations coefficients, all HRV indices showed an increase in mean values (t values from 3.79 to 14.94, p < 0.001) except the RR and HF that presented a decrease (t = 4.14 and 5.96, p < 0.0001).

CONCLUSION

Our results indicate that proper ultra-short-term recording method can provide a quick and reliable source of cardiac autonomic nervous system assessment.

Timing of Coping Instruction Presentation for Real-time Acute Stress Management: Potential Implications for Improved Surgical Performance

Individual performance on complex healthcare tasks can be influenced by acutely stressful situations. Real-time biofeedback using passive physiological monitoring may help to better understand an individual's progression towards acute stress-induced performance decrement. Providing biofeedback at an appropriate time may provide learners within an indicator that their current performance is susceptible to a decrement, and offer the opportunity to intervene. We explored the presentation timing of coping instructions during an acutely stressful task. In this pilot study, we recorded and analyzed electrocardiography data surrounding coping instruction presentation on various time schedules while participants played a first-person shooter computer game. Around times of significantly elevated heart rate, an indicator of acute stress, presenting a coping instruction tended to result in an increase in heart rate variability (HRV) following its presentation, with a more marked effect in high-stress conditions; not presenting a coping instruction at this time tended to result in a decrease in HRV in high-stress conditions, and no change in low-stress conditions. HRV following instruction presentation tended to increase in both high- and low-stress conditions when the instruction was presented at times of elevated heart rate; there was very little change in HRV when instruction presentation was not bound to physiology. Performance data showed that better performance was associated with greater adherence to coping instructions, compared to when zero instructions were followed. Implications for healthcare are significant, as acute stress is constant and it is necessary for providers to maintain a high level of performance.

Ultra-short-term heart rate variability during resistance exercise in the elderly

Despite the appeal of ultra-short-term heart rate variability (HRV) methods of analysis applied in the clinical and research settings, the number of studies that have investigated HRV by analyzing R-R interval (RRi) recordings shorter than 5 min is still limited. Moreover, ultra-short-term HRV analysis has not been extensively validated during exercise and, currently, no indications exist for its applicability during resistance exercise. The aim of the present study was to compare ultra-short-term HRV analysis with standard short-term HRV analysis during low-intensity, dynamic, lower limb resistance exercise in healthy elderly subjects. Heart rate (HR) and RRi signals were collected from 9 healthy elderly men during discontinuous incremental resistance exercise consisting of 4-min intervals at low intensities (10, 20, 30, and 35% of 1-repetition maximum). The original RRi signals were segmented into 1-, 2-, and 3-min sections. HRV was analyzed in the time domain (root mean square of the of differences between adjacent RRi, divided by the number of RRi, minus one [RMSSD]), RRi mean value and standard deviation [SDNN] (percentage of differences between adjacent NN intervals that are greater than 50 ms [pNN50]), and by non-linear analysis (short-term RRi standard deviation [SD1] and long-term RRi standard deviation [SD2]). No significant difference was found at any exercise intensity between the results of ultra-short-term HRV analysis and the results of standard short-term HRV analysis. Furthermore, we observed excellent (0.70 to 0.89) to near-perfect (0.90 to 1.00) concordance between linear and non-linear parameters calculated over 1- and 2-min signal sections and parameters calculated over 3-min signal sections. Ultra-short-term HRV analysis appears to be a reliable surrogate of standard short-term HRV analysis during resistance exercise in healthy elderly subjects.

Self-assessment of surgical ward crisis management using video replay augmented with stress biofeedback

Background

We aimed to explore the feasibility and attitudes towards using video replay augmented with real time stress quantification for the self-assessment of clinical skills during simulated surgical ward crisis management.

Methods

Twenty two clinicians participated in 3 different simulated ward based scenarios of deteriorating post-operative patients. Continuous ECG recordings were made for all participants to monitor stress levels using heart rate variability (HRV) indices. Video recordings of simulated scenarios augmented with real time stress biofeedback were replayed to participants. They were then asked to self-assess their performance using an objective assessment tool. Participants attitudes were explored using a post study questionnaire.

Results

Using HRV stress indices, we demonstrated higher stress levels in novice participants. Self-assessment scores were significantly higher in more experienced participants. Overall, participants felt that video replay and augmented stress biofeedback were useful in self-assessment.

Conclusion

Self-assessment using an objective self-assessment tool alongside video replay augmented with stress biofeedback is feasible in a simulated setting and well liked by participants.

Electronic supplementary material

The online version of this article (10.1186/s13037-018-0153-5) contains supplementary material, which is available to authorized users.

Are ultra-short heart rate variability features good surrogates of short-term ones? State-of-the-art review and recommendations

Ultra-short heart rate variability (HRV) analysis refers to the study of HRV features in excerpts of length <5 min. Ultra-short HRV is widely growing in many healthcare applications for monitoring individual's health and well-being status, especially in combination with wearable sensors, mobile phones, and smart-watches. Long-term (nominally 24 h) and short-term (nominally 5 min) HRV features have been widely investigated, physiologically justified and clear guidelines for analysing HRV in 5 min or 24 h are available. Conversely, the reliability of ultra-short HRV features remains unclear and many investigations have adopted ultra-short HRV analysis without questioning its validity. This is partially due to the lack of accepted algorithms guiding investigators to systematically assess ultra-short HRV reliability. This Letter critically reviewed the existing literature, aiming to identify the most suitable algorithms, and harmonise them to suggest a standard protocol that scholars may use as a reference in future studies. The results of the literature review were surprising, because, among the 29 reviewed papers, only one paper used a rigorous method, whereas the others employed methods that were partially or completely unreliable due to the incorrect use of statistical tests. This Letter provides recommendations on how to assess ultra-short HRV features reliably and proposes an inclusive algorithm that summarises the state-of-the-art knowledge in this area.

Ultra-shortened time-domain HRV parameters at rest and following exercise in athletes: an alternative to frequency computation of sympathovagal balance

PURPOSE

The primary purpose of this study was to determine the accuracy of the standard deviation of normal-to-normal intervals (SDNN) to root mean square of successive normal-to-normal interval differences (RMSSD) ratio from 1-min recordings (SDNN:RMSSD_1-min) compared to criterion recordings, as well as its relationship to low-frequency-to-high-frequency ratio (LF:HF) at rest and following maximal exercise in a group of collegiate athletes.

METHOD

Twenty athletes participated in the study. Heart rate variability (HRV) data were measured for 5 min before and at 5-10 and 25-30 min following a maximal exercise test. From each 5-min segment, the frequency-domain measures of HF, LF, and LF:HF ratio were analyzed. Time-domain measures of SDNN, RMSSD, and SDNN:RMSSD ratio were also analyzed from each 5-min segment, as well as from randomly selected 1-min recordings.

RESULT

The 1-min values of SDNN, RMSSD, and SDNN:RMSSD provided no significant differences and nearly perfect intra-class correlations (ICCs ranged from 0.97 to 1.00, p < 0.001 for all) to the criterion measures from 5-min recordings. In addition, SDNN, RMSSD, and SDNN:RMSSD from the 1-min segments provided very large to nearly perfect correlations (r values ranged from 0.71 to 0.97, p < 0.001 for all) to LF, HF, and LF:HF, respectively, at each time point.

CONCLUSION

The findings of the study suggest that ultra-shortened time-domain markers may be useful surrogates of the frequency-domain parameters for tracking changes in sympathovagal activity in athletes.

Detection of sympathoadrenal discharge by parameterisation of skin conductance and ECG measurement

Detection of sympathoadrenal discharge is valuable for stress monitoring, but measuring the circulating adrenaline level directly is inconvenient, making non-invasive physiological sensors an attractive alternative. Little is known however, about their performance in detecting different adrenaline levels. In this study, adrenaline measurements over time from 20 subjects × 2 trials were compared with skin conductance (SC) from different skin sites and ECG recordings from which the heart rate and QT interval were derived. The frequency of sudomotor responses (FSR) was derived from the SC recording, and a new composite parameter for amplification of synchronous changes in multiple sensor signals was calculated for different combinations of FSR from different skin sites, heart rate and QT interval. The single and composite parameters were evaluated for detection performance of adrenaline levels above 1000, 1500 and 2000 pmol/L. The best prediction performance was indicated for the composite parameter using the FSR from the abdomen, FSR from the forehead and the heart rate, with a ROC area under the curve of 0.93 for the 2000 pmol/L threshold. In conclusion, detection of strong sympathoadrenal discharges is feasible with good accuracy during resting conditions in comfortable room temperature.

An Overview of Heart Rate Variability Metrics and Norms

Healthy biological systems exhibit complex patterns of variability that can be described by mathematical chaos. Heart rate variability (HRV) consists of changes in the time intervals between consecutive heartbeats called interbeat intervals (IBIs). A healthy heart is not a metronome. The oscillations of a healthy heart are complex and constantly changing, which allow the cardiovascular system to rapidly adjust to sudden physical and psychological challenges to homeostasis. This article briefly reviews current perspectives on the mechanisms that generate 24 h, short-term (~5 min), and ultra-short-term (<5 min) HRV, the importance of HRV, and its implications for health and performance. The authors provide an overview of widely-used HRV time-domain, frequency-domain, and non-linear metrics. Time-domain indices quantify the amount of HRV observed during monitoring periods that may range from ~2 min to 24 h. Frequency-domain values calculate the absolute or relative amount of signal energy within component bands. Non-linear measurements quantify the unpredictability and complexity of a series of IBIs. The authors survey published normative values for clinical, healthy, and optimal performance populations. They stress the importance of measurement context, including recording period length, subject age, and sex, on baseline HRV values. They caution that 24 h, short-term, and ultra-short-term normative values are not interchangeable. They encourage professionals to supplement published norms with findings from their own specialized populations. Finally, the authors provide an overview of HRV assessment strategies for clinical and optimal performance interventions.

Detection of mental stress due to oral academic examination via ultra-short-term HRV analysis

Mental stress may cause cognitive dysfunctions, cardiovascular disorders and depression. Mental stress detection via short-term Heart Rate Variability (HRV) analysis has been widely explored in the last years, while ultra-short term (less than 5 minutes) HRV has been not. This study aims to detect mental stress using linear and non-linear HRV features extracted from 3 minutes ECG excerpts recorded from 42 university students, during oral examination (stress) and at rest after a vacation. HRV features were then extracted and analyzed according to the literature using validated software tools. Statistical and data mining analysis were then performed on the extracted HRV features. The best performing machine learning method was the C4.5 tree algorithm, which discriminated between stress and rest with sensitivity, specificity and accuracy rate of 78%, 80% and 79% respectively.

Improving the Test-Retest Reliability of Resting State fMRI by Removing the Impact of Sleep

Resting state functional magnetic resonance imaging (rs-fMRI) provides a powerful tool to examine large-scale neural networks in the human brain and their disturbances in neuropsychiatric disorders. Thanks to its low demand and high tolerance, resting state paradigms can be easily acquired from clinical population. However, due to the unconstrained nature, resting state paradigm is associated with excessive head movement and proneness to sleep. Consequently, the test-retest reliability of rs-fMRI measures is moderate at best, falling short of widespread use in the clinic. Here, we characterized the effect of sleep on the test-retest reliability of rs-fMRI. Using measures of heart rate variability (HRV) derived from simultaneous electrocardiogram (ECG) recording, we identified portions of fMRI data when subjects were more alert or sleepy, and examined their effects on the test-retest reliability of functional connectivity measures. When volumes of sleep were excluded, the reliability of rs-fMRI is significantly improved, and the improvement appears to be general across brain networks. The amount of improvement is robust with the removal of as much as 60% volumes of sleepiness. Therefore, test-retest reliability of rs-fMRI is affected by sleep and could be improved by excluding volumes of sleepiness as indexed by HRV. Our results suggest a novel and practical method to improve test-retest reliability of rs-fMRI measures.

Reliability of Lagged Poincaré Plot Parameters in Ultrashort Heart Rate Variability Series: Application on Affective Sounds

The number of studies about ultrashort cardiovascular time series is increasing because of the demand for mobile applications in telemedicine and e-health monitoring. However, the current literature still needs a proper validation of heartbeat nonlinear dynamics assessment from ultrashort time series. This paper reports on the reliability of the Lagged Poincaré Plot (LPP) parameters-calculated from ultrashort cardiovascular time series. Reliability is studied on simulated as well as on real RR series. Simulated RR series are generated and LPP parameters estimated for ultrashort time series (from 15 to 60 s) are compared to those estimated from 1 h. All LPP parameters estimated from time series longer than 35 s presented a Spearman's correlation coefficient higher than 0.99. RR series acquired from 32 healthy subjects during 5-min resting state sessions are used to test the LPP approach in experimental data. The usefulness of ultrashort term parameters in real data is accomplished also studying their ability to discriminate positive and negative valence of auditory stimuli taken from the International Affective Digitized Sound System (IADS) dataset. The achieved accuracies in the recognition of elicitation along the valence dimension, using only the LPP parameters, were of 77.78% for 1 min 28 s series, and of 79.17% for 35 s series.

Heart Rate Variability and Cardiac Vagal Tone in Psychophysiological Research - Recommendations for Experiment Planning, Data Analysis, and Data Reporting

Psychophysiological research integrating heart rate variability (HRV) has increased during the last two decades, particularly given the fact that HRV is able to index cardiac vagal tone. Cardiac vagal tone, which represents the contribution of the parasympathetic nervous system to cardiac regulation, is acknowledged to be linked with many phenomena relevant for psychophysiological research, including self-regulation at the cognitive, emotional, social, and health levels. The ease of HRV collection and measurement coupled with the fact it is relatively affordable, non-invasive and pain free makes it widely accessible to many researchers. This ease of access should not obscure the difficulty of interpretation of HRV findings that can be easily misconstrued, however, this can be controlled to some extent through correct methodological processes. Standards of measurement were developed two decades ago by a Task Force within HRV research, and recent reviews updated several aspects of the Task Force paper. However, many methodological aspects related to HRV in psychophysiological research have to be considered if one aims to be able to draw sound conclusions, which makes it difficult to interpret findings and to compare results across laboratories. Those methodological issues have mainly been discussed in separate outlets, making difficult to get a grasp on them, and thus this paper aims to address this issue. It will help to provide psychophysiological researchers with recommendations and practical advice concerning experimental designs, data analysis, and data reporting. This will ensure that researchers starting a project with HRV and cardiac vagal tone are well informed regarding methodological considerations in order for their findings to contribute to knowledge advancement in their field.

Sinabro: A Smartphone-Integrated Opportunistic Electrocardiogram Monitoring System

In our preliminary study, we proposed a smartphone-integrated, unobtrusive electrocardiogram (ECG) monitoring system, Sinabro, which monitors a user's ECG opportunistically during daily smartphone use without explicit user intervention. The proposed system also monitors ECG-derived features, such as heart rate (HR) and heart rate variability (HRV), to support the pervasive healthcare apps for smartphones based on the user's high-level contexts, such as stress and affective state levels. In this study, we have extended the Sinabro system by: (1) upgrading the sensor device; (2) improving the feature extraction process; and (3) evaluating extensions of the system. We evaluated these extensions with a good set of algorithm parameters that were suggested based on empirical analyses. The results showed that the system could capture ECG reliably and extract highly accurate ECG-derived features with a reasonable rate of data drop during the user's daily smartphone use.

SenseMyHeart: A cloud service and API for wearable heart monitors

In the era of ubiquitous computing, the growing adoption of wearable systems and body sensor networks is trailing the path for new research and software for cardiovascular intensity, energy expenditure and stress and fatigue detection through cardiovascular monitoring. Several systems have received clinical-certification and provide huge amounts of reliable heart-related data in a continuous basis. PhysioNet provides equally reliable open-source software tools for ECG processing and analysis that can be combined with these devices. However, this software remains difficult to use in a mobile environment and for researchers unfamiliar with Linux-based systems. In the present paper we present an approach that aims at tackling these limitations by developing a cloud service that provides an API for a PhysioNet-based pipeline for ECG processing and Heart Rate Variability measurement. We describe the proposed solution, along with its advantages and tradeoffs. We also present some client tools (windows and Android) and several projects where the developed cloud service has been used successfully as a standard for Heart Rate and Heart Rate Variability studies in different scenarios.

Comparing stress markers across various cohorts in a mobile setting

There exist multiple markers for measuring psychological stress, with varying specificities and sensitivities. However, in a real-life setting, there is limited data on how robust these methods may be especially in a relatively mobile context where the signal fidelity maybe limited. Thus any large scale data to inform how these methods perform, using commonly available sensors, based on both context and cohort characterization, can greatly add to our knowledge of their respective utility in real-life settings. This paper presents a study of 253 subjects which provides crucial data for analysing various stress markers in a mobile setting. We also provide early analysis results.

Unobtrusive monitoring of ECG-derived features during daily smartphone use

Heart rate variability (HRV) is known to be one of the representative ECG-derived features that are useful for diverse pervasive healthcare applications. The advancement in daily physiological monitoring technology is enabling monitoring of HRV in people's everyday lives. In this study, we evaluate the feasibility of measuring ECG-derived features such as HRV, only using the smartphone-integrated ECG sensors system named Sinabro. We conducted the evaluation with 13 subjects in five predetermined smartphone use cases. The result shows the potential that the smartphone-based sensing system can support daily monitoring of ECG-derived features; The average errors of HRV over all participants ranged from 1.65% to 5.83% (SD: 2.54~10.87) for five use cases. Also, all of individual HRV parameters showed less than 5% of average errors for the three reliable cases.

Probing ECG-based mental state monitoring on short time segments

Electrocardiography is used to provide features for mental state monitoring systems. There is a need for quick mental state assessment in some applications such as attentive user interfaces. We analyzed how heart rate and heart rate variability features are influenced by working memory load (WKL) and time-on-task (TOT) on very short time segments (5s) with both statistical significance and classification performance results. It is shown that classification of such mental states can be performed on very short time segments and that heart rate is more predictive of TOT level than heart rate variability. However, both features are efficient for WKL level classification. What's more, interesting interaction effects are uncovered: TOT influences WKL level classification either favorably when based on HR, or adversely when based on HRV. Implications for mental state monitoring are discussed.

Validity of the ithlete™ Smart Phone Application for Determining Ultra-Short-Term Heart Rate Variability

The purpose of this investigation was to cross-validate the ithlete™ heart rate variability smart phone application with an electrocardiograph for determining ultra-short-term root mean square of successive R-R intervals. The root mean square of successive R-R intervals was simultaneously determined via electrocardiograph and ithlete™ at rest in twenty five healthy participants. There were no significant differences between the electrocardiograph and ithlete™ derived root mean square of successive R-R interval values (p > 0.05) and the correlation was near perfect (r = 0.99, p < 0.001). In addition, the ithlete™ revealed a Standard Error of the Estimate of 1.47 and Bland Altman plot showed that the limits of agreement ranged from 2.57 below to 2.63 above the constant error of −0.03. In conclusion, the ithlete™ appeared to provide a suitably accurate measure of root mean square of successive R-R intervals when compared to the electrocardiograph measures obtained in the laboratory within the current sample of healthy adult participants. The current study lays groundwork for future research determining the efficacy of ithlete™ for reflecting athletic training status over a chronic conditioning period.

Association of autonomic nervous system and EEG scalp potential during playing 2D Grand Turismo 5

Cerebral activation and autonomic nervous system have importance in studies such as mental stress. The aim of this study is to analyze variations in EEG scalp potential which may influence autonomic activation of heart while playing video games. Ten healthy participants were recruited in this study. Electroencephalogram (EEG) and electrocardiogram (ECG) signals were measured simultaneously during playing video game and rest conditions. Sympathetic and parasympathetic innervations of heart were evaluated from heart rate variability (HRV), derived from the ECG. Scalp potential was measured by the EEG. The results showed a significant upsurge in the value theta Fz/alpha Pz (p&#60;0.001) while playing game. The results also showed tachycardia while playing video game as compared to rest condition (p&#60;0.005). Normalized low frequency power and ratio of low frequency/high frequency power were significantly increased while playing video game and normalized high frequency power sank during video games. Results showed synchronized activity of cerebellum and sympathetic and parasympathetic innervation of heart.

Heart rate variability indices for very short-term (30 beat) analysis. Part 1: survey and toolbox

Heart rate variability (HRV) analysis over very short (<60 s) periods may be useful for monitoring dynamic changes in autonomic nervous system activity where steady-state conditions are not maintained (e.g. during drug administration, or the start or end of exercise). From the 1980s there has been a wealth of HRV indices produced in the quest for better measures of the change in parasympathetic and sympathetic activity. Many of the indices have been sparingly used and have not been investigated for application to short-term use. This study surveyed published methods of HRV analysis searching for indices that could be applied to very short time HRV analysis. The survey included measures of time domain, frequency domain, respiratory sinus arrhythmia, Poincaré plot, and heart rate characteristics. Indices were tested with short segments of archived data to remove those that produced invalid results, or were mathematically equivalent to, but less well known than other indices. The survey identified a comprehensive list of 115 indices that were subsequently coded and screened. Of these, 70 were unique and produced a finite number with 60 s data, so are included in the Toolbox. These indices require validation against physiological data before they can be applied to short-term HRV analysis of cardiac autonomic nervous system activity.

Algorithms based on CWT and classifiers to control cardiac alterations and stress using an ECG and a SCR

This paper presents the results of using a commercial pulsimeter as an electrocardiogram (ECG) for wireless detection of cardiac alterations and stress levels for home control. For these purposes, signal processing techniques (Continuous Wavelet Transform (CWT) and J48) have been used, respectively. The designed algorithm analyses the ECG signal and is able to detect the heart rate (99.42%), arrhythmia (93.48%) and extrasystoles (99.29%). The detection of stress level is complemented with Skin Conductance Response (SCR), whose success is 94.02%. The heart rate variability does not show added value to the stress detection in this case. With this pulsimeter, it is possible to prevent and detect anomalies for a non-intrusive way associated to a telemedicine system. It is also possible to use it during physical activity due to the fact the CWT minimizes the motion artifacts.