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Purpose: Moderate-intensity aerobic exercise is safe and beneficial in atrial fibrillation (AF) and coronary heart disease (CHD). Irregular or rapid heart rates (HR) in AF and other heart conditions create a challenge to using HR to monitor exercise intensity. The purpose of this study was to assess the potential of breathing frequency (BF) to monitor exercise intensity in people with AF and CHD without AF. Methods: This observational study included 30 AF participants (19 Male, 70.7 ± 8.7 yrs) and 67 non-AF CHD participants (38 Male, 56.9 ± 11.4 yrs). All performed an incremental maximal exercise test with pulmonary gas exchange. Results: Peak aerobic power in AF ( V ˙ O2peak; 17.8 ± 5.0 ml.kg−1.min−1) was lower than in CHD (26.7 ml.kg−1.min−1) (p <.001). BF responses in AF and CHD were similar (BF peak: AF 34.6 ± 5.4 and CHD 36.5 ± 5.0 breaths.min−1; p =.106); at the 1st ventilatory threshold (BF@VT-1: AF 23.2 ± 4.6; CHD 22.4 ± 4.6 breaths.min−1; p =.240). % V ˙ O2peak at VT-1 were similar in AF and CHD (AF: 59%; CHD: 57%; p =.656). Conclusion: With the use of wearable technologies on the rise, that now include BF, this first study provides an encouraging potential for BF to be used in AF and CHD. As the supporting data are based on incremental ramp protocol results, further research is required to assess BF validity to manage exercise intensity during longer bouts of exercise. [ABSTRACT FROM AUTHOR]

Aircraft pilots face a high mental workload (MW) under environmental constraints induced by high altitude and sometimes sleep restriction (SR). Our aim was to assess the combined effects of hypoxia and sleep restriction on cognitive and physiological responses to different MW levels using the Multi-Attribute Test Battery (MATB)-II with an additional auditory Oddball-like task. Seventeen healthy subjects were subjected in random order to three 12-min periods of increased MW level (low, medium, and high): sleep restriction (SR, <3 h of total sleep time (TST)) vs. habitual sleep (HS, >6 h TST), hypoxia (HY, 2 h, FIO2 = 13.6%, ~3500 m vs. normoxia, NO, FIO2 = 21%). Following each MW level, participants completed the NASA-TLX subjective MW scale. Increasing MW decreases performance on the MATB-II Tracking task (p = 0.001, MW difficulty main effect) and increases NASA-TLX (p = 0.001). In the combined HY/SR condition, MATB-II performance was lower, and the NASA-TLX score was higher compared with the NO/HS condition, while no effect of hypoxia alone was observed. In the accuracy of the auditory task, there is a significant interaction between hypoxia and MW difficulty (F(2–176) = 3.14, p = 0.04), with lower values at high MW under hypoxic conditions. Breathing rate, pupil size, and amplitude of pupil dilation response (PDR) to auditory stimuli are associated with increased MW. These parameters are the best predictors of increased MW, independently of physiological constraints. Adding ECG, SpO2, or electrodermal conductance does not improve model performance. In conclusion, hypoxia and sleep restriction have an additive effect on MW. Physiological and electrophysiological responses must be taken into account when designing a MW predictive model and cross-validation. [ABSTRACT FROM AUTHOR]

This paper introduces and evaluates an innovative sensor for unobtrusive in-car respiration monitoring, mounted on the backrest of the driver's seat. The sensor seamlessly integrates into the vehicle, measuring breathing rates continuously without requiring active participation from the driver. The paper proves the feasibility of unobtrusive in-car measurements over long periods of time. Operation of the sensor was investigated over 12 participants sitting in the driver seat. A total of 107 min of driving in diverse conditions with overall coverage rate of 84.45% underscores the sensor potential to reliably capture physiological changes in breathing rate for fatigue and stress detection. [ABSTRACT FROM AUTHOR]

Vital sign monitoring is dominated by precise but costly contact-based sensors. Contactless devices such as radars provide a promising alternative. In this article, the effects of lateral radar positions on breathing and heartbeat extraction are evaluated based on a sleep study. A lateral radar position is a radar placement from which multiple human body zones are mapped onto different radar range sections. These body zones can be used to extract breathing and heartbeat motions independently from one another via these different range sections. Radars were positioned above the bed as a conventional approach and on a bedside table as well as at the foot end of the bed as lateral positions. These positions were evaluated based on six nights of sleep collected from healthy volunteers with polysomnography (PSG) as a reference system. For breathing extraction, comparable results were observed for all three radar positions. For heartbeat extraction, a higher level of agreement between the radar foot end position and the PSG was found. An example of the distinction between thoracic and abdominal breathing using a lateral radar position is shown. Lateral radar positions could lead to a more detailed analysis of movements along the body, with the potential for diagnostic applications. [ABSTRACT FROM AUTHOR]

Objectives: Chronic musculoskeletal pain is associated with decreased parasympathetic and increased sympathetic activity in the autonomic nervous system. The objective of this study was to determine the associations between objective measures of heart rate variability (a measure of autonomic nervous system function), actigraphy (a measure of activity and sleep quality), respiration rates, and subjective patient‐reported outcome measures (PROMs) of central sensitization, kinesiophobia, disability, the effect of pain on sleep, and life quality. Methods: Thirty‐eight study subjects were divided into two subgroups, including low symptoms of central sensitization (n = 18) and high symptoms of central sensitization (n = 20), based on patient‐reported scores on the Central Sensitization Inventory (CSI). Heart rate variability (HRV) and actigraphy measurements were carried out simultaneously in 24 h measurement during wakefulness and sleep. Results: A decrease in HRV during the first 2 h of sleep was stronger in the low CSI subgroup compared to the high CSI subgroup. Otherwise, all other HRV and actigraphy parameters and subjective measures of central sensitization, disability, kinesiophobia, the effect of pain on sleep, and quality of life showed only little associations. Discussion: The high CSI subgroup reported significantly more severe symptoms of disability, kinesiophobia, sleep, and quality of life compared to the low CSI subgroup. However, there were only small and nonsignificant trend in increased sympathetic nervous system activity and poorer sleep quality on the high central sensitization subgroup. Moreover, very little differences in respiratory rates were found between the groups. [ABSTRACT FROM AUTHOR]

Sleep regularity and chronotype can affect health, performance, and overall well-being. This observational study examines how sleep regularity and chronotype affect sleep quality and cardiorespiratory metrics. Data was collected from 1 January 2019 through 30 December 2019 from over 330 000 Sleep Number smart bed users across the United States who opted into this at-home study. A pressure signal from the smart bed reflected bed presence, movements, heart rate (HR), and breathing rate (BR). Participants (mean age: 55.69 years [SD: 14.0]; 51.2% female) were categorized by chronotype (16.8% early; 62.2% intermediate, 20.9% late) and regularity of sleep timing. Participants who were regular sleepers (66.1%) experienced higher percent restful sleep and lower mean HR and BR compared to the 4.8% categorized as irregular sleepers. Regular early-chronotype participants displayed better sleep and cardiorespiratory parameters compared to those with regular late-chronotypes. Significant variations were noted in sleep duration (Cohen's d = 1.54 and 0.88, respectively) and restful sleep (Cohen's d = 1.46 and 0.82, respectively) between early and late chronotypes, particularly within regular and irregular sleep patterns. This study highlights how sleep regularity and chronotype influence sleep quality and cardiorespiratory metrics. Irrespective of chronotype, sleep regularity demonstrated a substantial effect. Further research is necessary to confirm these findings. [ABSTRACT FROM AUTHOR]

Abnormalities in cardiorespiratory measurements have repeatedly been found in patients with panic disorder (PD) during laboratory-based assessments. However, recordings performed outside laboratory settings are required to test the ecological validity of these findings. Wearable devices, such as sensor-imbedded garments, biopatches, and smartwatches, are promising tools for this purpose. We systematically reviewed the evidence for wearables-based cardiorespiratory assessments in PD by searching for publications on the PubMed, PsycINFO, and Embase databases, from inception to 30 July 2022. After the screening of two-hundred and twenty records, eight studies were included. The limited number of available studies and critical aspects related to the uncertain reliability of wearables-based assessments, especially concerning respiration, prevented us from drawing conclusions about the cardiorespiratory function of patients with PD in daily life. We also present preliminary data on a pilot study conducted on volunteers at the Villa San Benedetto Menni Hospital for evaluating the accuracy of heart rate (HR) and breathing rate (BR) measurements by the wearable Zephyr BioPatch compared with the Quark-b2 stationary testing system. Our exploratory results suggested possible BR and HR misestimation by the wearable Zephyr BioPatch compared with the Quark-b2 system. Challenges of wearables-based cardiorespiratory assessment and possible solutions to improve their reliability and optimize their significant potential for the study of PD pathophysiology are presented. [ABSTRACT FROM AUTHOR]

This work proposes a computational algorithm which extracts the frequency, timings and signal segments corresponding to respiratory phases, through buccal and nasal acoustic signal processing. The proposal offers a computational solution for medical applications which require on-site or remote patient monitoring and evaluation of pulmonary pathologies, such as coronavirus disease 2019 (COVID-19). The state of the art presents a few respiratory evaluation proposals through buccal and nasal acoustic signals. Most proposals focus on respiratory signals acquired by a medical professional, using stethoscopes and electrodes located on the thorax. In this case the signal acquisition process is carried out through the use of a low cost and easy to use mask, which is equipped with strategically positioned and connected electret microphones, to maximize the proposed algorithm’s performance. The algorithm employs signal processing techniques such as signal envelope detection, decimation, fast Fourier transform (FFT) and detection of peaks and time intervals via estimation of local maxima and minima in a signal’s envelope. For the validation process a database of 32 signals of different respiratory modes and frequencies was used. Results show a maximum average error of 2.23% for breathing rate, 2.81% for expiration time and 3.47% for inspiration time. [ABSTRACT FROM AUTHOR]

Plant secondary metabolites in Tetrapleuratetraptera fruit meal (TTFM) could be exploited as natural safe feed additives alternative to non-nutritive chemical and antibiotics to enhance rumen fermentation and feed utilization efficiency with negative oxidative stress. The study investigated the responses of West African Dwarf (WAD) rams to the diets containing varying levels of supplemental Tetrapleura tetraptera fruit meal on the oxidative status and thermo-physiological parameters. Thirty five (35) WAD rams with an average live weight of 13.20±0.2 kg were used in a completely randomized design for 140 days. Five concentrate diets containing varying levels (0, 0.5, 1.0, 1.5 and 2.0 %) of TTFM were formulated while Panicum maximum was fed as a basal diet. The rectal temperature, pulse rate and respiratory rate were measured at two week intervals while blood was collected through jugular veins at the onset and at the end of feeding trial to determine oxidative status parameters in terms of superoxide dismutase (SOD), glutathione peroxidase, thiobarbituric acid reactive substance (TBARS). Data collected were subjected to one way Analysis of Variance (ANOVA). No significant difference observed (p>0.05) in the rectal temperature while the pulse rate and breathing rate increased significantly (p<0.05) till 12th week of the experiment. Increased glutathione peroxidase, superoxide dismutase and lowered thiobarbituric acid reactive substance were recorded on the rams at the end of the feeding trial. It was therefore concluded that inclusion of Tetrapleura tetraptera up to 2 % level reduced the oxidative stress in the experimental animals coupled with better thermo-physiological responses. [ABSTRACT FROM AUTHOR]

Vital sign monitoring is fundamental in daily patient care. Wearable technology has become an important tool in personalized medicine; however, there is a tradeoff problem between accuracy and comfort caused by the elastic textile used. Our study sought to examine the accuracy of heart rate (HR) and breathing rate (BR) monitoring as measured by iSmartweaR, a novel wearable and wireless sensor that uses the laser Doppler technique, with special focus on sex and different static postures. A total of 30 male and 23 female patients, including 23 patients with cardiovascular diseases and 30 healthy participants, were recruited. The HR and BR were measured with iSmartweaR and the gold standard method (CardioSoft and MasterScreen™ CPX) simultaneously in three static postures (supine rest, right lateral decubitus, and left lateral decubitus). The mean errors of HR and BR between iSmartweaR and the gold standard method were close to zero (0.55 and 0.71, respectively). The standard deviations of the HR error and BR error were 0.59 and 0.67, respectively. Disease, posture, sex, and measurement time did not cause clinically significant measurement errors. The overall percentage of missing data was 7.01%. The use of iSmartweaR was valid across different sexes and static postures. It is real-time, continuous and manpower-saving, and can be applied in patient care. [ABSTRACT FROM AUTHOR]

Breathing interventions have been shown to improve sport performance. Although evidence exists to support the role of perceived arousal as a critical underlying mechanism of breathing interventions, methodological differences in the literature preclude clear understanding of potential contributing factors to the effectiveness of such interventions. Under neutral contexts, we have demonstrated attention, dyspnea, and hindrance may need to be considered as mediators of how breathing frequency affects motor performance. We sought to extend our previous findings to determine how breathing frequency affects motor performance under varying emotional conditions. Participants (N =35, Mage =21.68, SD =2.96; 20 females) performed slow, normal, and fast metronomepaced breathing while viewing pleasant and unpleasant stimuli prior to executing a pinch grip task. Performance was assessed via reaction time (RT), variability (V) and error (AE). Assessment of indices of perceived arousal included measuring heart rate variability (HRV) and visual analog scale responses. Visual analog scales were also used to assess attention, dyspnea, and hindrance. Repeated measures ANOVAs showed slow breathing increased RT and HRV compared to normal and fast breathing under emotional conditions (all p's<0.05). Hierarchical multiple regression models revealed that decreased breathing frequency predicted increases in RT (β = -0.25, p <0.05) under pleasant conditions, while predicting increases in HRV for unpleasant conditions (β = -0.45, p <0.001). Increases in dyspnea (β =0.29, p <0.05) and hindrance (β =0.35, p <0.01) predicted increases in RT under pleasant conditions, while only increases in hindrance predicted increases in RT under unpleasant conditions (β =0.41, p <0.01). Decreases in breathing frequency predicted increases in HRV under unpleasant conditions (β = -0.45, p <0.001). Overall, our findings suggest under varying emotional contexts breathing frequency differentially affects movement, potentially mediated by factors other than perceived arousal. In addition, these results inform the use of breath regulation as an antecedent emotion regulation strategy. [ABSTRACT FROM AUTHOR]

Background&Purpose: One of the most important goals of any athlete is optimum performance during competitions, and biofeedback is one of the new and growing ways to improve athletes' performance. This study investigated the effect of biofeedback exercises on some physiological factors and the performance of skilled archers. Method: The present study was a semi-experimental type with pre-test and post-test design and a control group. Twenty four young men with an average age of 22.5 years were purposefully recruited among the participants who attended national ranking archery competitions and randomly assigned to an experimental and a control group, each consisting of 12 participants. After the pre-test (archery performance test), the experimental group first attended 10 sessions of biofeedback training related to heart rate control and breathing control based on the training protocol and then attended 10 sessions of the same biofeedback exercises with skin conduction guidance. Both groups continued their routine archery exercises under the supervision of the researchers during this period. After completing 20 training sessions, the archery performance was taken again as the post-test with the same pretest condition from both groups. Results: The results showed that the heart rate of the participants was higher in tension and stress conditions compared to the resting state (p < 0.05). Also, at the post-test, the experimental group showed a lower heart rate under stress conditions compared to the control group (p < 0.05). Moreover, the analysis of covariance showed that the performance of both groups improved compared to the pre-test, but the experimental group performed better than the control group (p < 0.05). Conclusion: According to the findings of this study, more attention should be given to biofeedback exercises, and it is suggested to emphasize its use along with other training protocols. [ABSTRACT FROM AUTHOR]

The emotional reactions to social exclusion can be associated with physiological responses that could allow researchers to estimate the valence and intensity of the ongoing affective state. In this work, respiratory activity was analysed to verify whether breathing rate variations can be considered as predictive factors of subsequent positive and negative affect after inclusion and exclusion in young women. A standard Cyberball task was implemented and manipulated information was provided to the participants to create both conditions. The participants were socially excluded by limiting their participation to 6% of the total number of passes among three teammates and providing negative feedback about them. The results suggest that breathing rate can be a good option to infer subjective feelings during social interactions and a promising feature to incorporate into modern emotion monitoring systems as an alternative to other physiological measures. Furthermore, the interaction between metaemotion and physiology was studied by recording breathing rate while completing the Positive and Negative Affect Schedule, evidencing a breathing rate increase during the emotion self-assessment only after exclusion. [ABSTRACT FROM AUTHOR]

Competitive indoor climbing has increased in popularity at the youth, collegiate, and Olympic levels. A critical aspect for improving performance is characterizing the physiologic response to different climbing strategies (e.g., work/rest patterns, pacing) and techniques (e.g., body position and movement) relative to location on climbing wall with spatially varying characteristics (e.g., wall inclinations, position of foot/hand holds). However, this response is not well understood due to the limited capabilities of climbing-specific measurement and assessment tools. In this study, we developed a novel method to examine time-resolved sensor-based measurements of multiple personal biometrics at different microlocations (finely spaced positions; MLs) along a climbing route. For the ML-specific biometric system (MLBS), we integrated continuous data from wearable biometric sensors and smartphone-based video during climbing, with a customized visualization and analysis system to determine three physiologic parameters (heart rate, breathing rate, ventilation rate) and one body movement parameter (hip acceleration), which are automatically time-matched to the corresponding video frame to determine ML-specific biometrics. Key features include: (1) biometric sensors that are seamlessly embedded in the fabric of an athletic compression shirt, and do not interfere with climbing performance, (2) climbing video, and (3) an interactive graphical user interface to rapidly visualize and analyze the time-matched biometrics and climbing video, determine timing sequence between the biometrics at key events, and calculate summary statistics. To demonstrate the capabilities of MLBS, we examined the relationship between changes in ML-specific climbing characteristics and changes in the physiologic parameters. Our study demonstrates the ability of MLBS to determine multiple time-resolved biometrics at different MLs, in support of developing and assessing different climbing strategies and training methods to help improve performance. [ABSTRACT FROM AUTHOR]

Heart rate variability (HRV) is a noninvasive marker for the cardiovascular system and the substantial influence of this system on the autonomous nervous system (ANS). Contact and remote photoplethysmography (PPG) proved to be a reliable means of pulse rate variability (PRV) measurement as in wearable devices or imaging PPG using video cameras with evolved technical specifications. However, PRV effectiveness remains unclear during controlled breathing rate. Specifically, respiratory rate, among other physiological factors, applies profound effects on ANS balance. In this study, we demonstrated that PRV can be a reliable marker, as HRV, during various controlled breathing rates. We evaluated the proposed PRV monitoring study against reference HRV obtained from an ECG measurement device. PRV was measured from video streams captured using a smartphone and a webcam for the upper abdominal section of healthy volunteers while in standing position and performing six different respiratory maneuvers. We evaluated frequency and nonlinear HRV indices. Agreement between all indices was tested for camera-based devices and ground truth for all performed tasks. The agreement presented a consistent mean close to 95% limit of agreement (LoA). Also, we employed spectral analysis and measured the spectral divergences, normalized root mean squared error (NRMSE), and correlation. Kullback-Leibler Divergence median and percentiles results indicate congruent spectrums, where the maximum median was 0.18. A significant spectral correlation with minimum value of 0.98, and relatively low spectrum NRMSE of 0.24 were observed. Moreover, the results led to HRV physiological related phenomena which agreed with physiologically interpretations found in the literature. [ABSTRACT FROM AUTHOR]

Objective: Increased understanding of the role of cortical structures in respiratory control may help the understanding of seizure‐induced respiratory dysfunction that leads to sudden unexpected death in epilepsy (SUDEP). The aim of this study was to characterize respiratory responses to electrical stimulation (ES), including inhibition and enhancement of respiration. Methods: We prospectively recruited 19 consecutive patients with intractable epilepsy undergoing stereotactic electroencephalography (EEG) evaluation from June 2015 to June 2018. Inclusion criteria were patients ≥18 years in whom ES was indicated for clinical mapping of ictal onset or eloquent cortex as part of the presurgical evaluation. ES was carried out at 50 Hz, 0.2 msec, and 1–10 mA current intensity. Common brain regions sampled across all patients were amygdala (AMY), hippocampus (HG), anterior cingulate gyrus (CING), orbitofrontal cortex (OrbF), temporal neocortex (TNC), temporal pole (TP), and entorhinal cortex (ERC). Seven hundred fifty‐five stimulations were conducted. Quantitative analysis of breathing signal, that is, changes in breathing rate (BR), depth (TV), and minute ventilation (MV), was carried out during ES using the BreathMetrics breathing waveform analysis toolbox. Electrocardiography, arterial oxygen saturation, end‐tidal and transcutaneous carbon dioxide, nasal airflow, and abdominal and thoracic plethysmography were monitored continuously during stimulations. Results: Electrical stimulation of TP and CING (at lower current strengths <3 mA) increased TV and MV. At >7‐10 mA, CING decreased TV and MV. On the other hand, decreased TV and MV occurred with stimulation of mesial temporal structures such as AMY and HG. Breathing changes were dependent on stimulation intensity. Lateral temporal, entorhinal, and orbitofrontal cortices did not affect breathing either way. Significance: These findings suggest that breathing responses other than apnea can be induced by ES. Identification of two regions—the temporal pole and anterior cingulate gyrus—for enhancement of breathing may be important in paving the way to future development of strategies for prevention of SUDEP. [ABSTRACT FROM AUTHOR]

Waterless transportation for live grouper is a novel mode of transport that not only saves money, but also lowers wastewater pollution. Technical obstacles remain, however, in achieving intelligent monitoring and a greater survival rate. During live grouper waterless transportation, the stress response is a key indicator that affects the survival life-span of the grouper. Studies based on breathing rate analysis have demonstrated that among many stress response parameters, breathing rate is the most direct parameter to reflect the intensity. Conventional measurement methods, which set up sensors on the gills of groupers, interfere with the normal breathing of living aquatic products and are complex in system design. We designed a new breathing monitoring system based on a completely non-destructive approach. The system allows the real-time monitoring of living aquatic products' breathing rate by simply placing the millimeter wave radar on the inner wall of the incubator and facing the gills. The system we developed can detect more parameters in the future, and can replace the existing system to simplify the study of stress responses. [ABSTRACT FROM AUTHOR]

Purpose: To determine how repeatable thoracoabdominal compartmental contributions to tidal volume (VT) are across different intensities of exercise, and to examine if the pattern of breathing for a given minute ventilation (V̇E) is reproducible between constant-load and ramp exercise tests. Methods: Ten healthy adults (age: 27 ± 6 yr, peak oxygen uptake: 42 ± 5 mL min−1 kg−1) completed a 25 W·min−1 ramp cycling test to exhaustion and two repetitions of a step cycling test on separate days. VT, breathing rate (BR), and V̇E were assessed using a bi-directional turbine, and thoracic and abdominal contributions to VT were measured using respiratory inductance plethysmography. Repeatability (step vs. step) and reproducibility (step vs. ramp) of responses were assessed using the intra-class correlation coefficient (ICC). Results: The relative compartment contributions to VT during step exercise were highly repeatable for low (ICC = 0.87, p = 0.003), moderate (ICC = 0.89, p = 0.002) and heavy (ICC = 0.93, p = 0.001) exercise. Inter-individual differences in response to higher intensity exercise were observed, as two participants had significant relationships between VT and their percent compartmental contributions to VT, but five others did not. No differences were identified between ramp and step exercise tests for VT (main effect: p = 0.61) or BR (main effect: p = 0.77) at matched V̇E for each intensity, and the pattern of breathing was reproducible for each intensity. Conclusion: These findings suggest the way young healthy adults breathe for a given V̇E is repeatable day-to-day and reproducible between different exercise protocols. [ABSTRACT FROM AUTHOR]

The Sleep Number smart bed uses embedded ballistocardiography, together with network connectivity, signal processing, and machine learning, to detect heart rate (HR), breathing rate (BR), and sleep vs. wake states. This study evaluated the performance of the smart bed relative to polysomnography (PSG) in estimating epoch-by-epoch HR, BR, sleep vs. wake, mean overnight HR and BR, and summary sleep variables. Forty-five participants (aged 22–64 years; 55% women) slept one night on the smart bed with standard PSG. Smart bed data were compared to PSG by Bland–Altman analysis and Pearson correlation for epoch-by-epoch HR and epoch-by-epoch BR. Agreement in sleep vs. wake classification was quantified using Cohen's kappa, ROC analysis, sensitivity, specificity, accuracy, and precision. Epoch-by-epoch HR and BR were highly correlated with PSG (HR: r = 0.81, |bias| = 0.23 beats/min; BR: r = 0.71, |bias| = 0.08 breaths/min), as were estimations of mean overnight HR and BR (HR: r = 0.94, |bias| = 0.15 beats/min; BR: r = 0.96, |bias| = 0.09 breaths/min). Calculated agreement for sleep vs. wake detection included kappa (prevalence and bias-adjusted) = 0.74 ± 0.11, AUC = 0.86, sensitivity = 0.94 ± 0.05, specificity = 0.48 ± 0.18, accuracy = 0.86 ± 0.11, and precision = 0.90 ± 0.06. For all-night summary variables, agreement was moderate to strong. Overall, the findings suggest that the Sleep Number smart bed may provide reliable metrics to unobtrusively characterize human sleep under real life-conditions. [ABSTRACT FROM AUTHOR]

Breathing rate is considered one of the fundamental vital signs and a highly informative indicator of physiological state. Given that the monitoring of heart activity is less complex than the monitoring of breathing, a variety of algorithms have been developed to estimate breathing activity from heart activity. However, estimating breathing rate from heart activity outside of laboratory conditions is still a challenge. The challenge is even greater when new wearable devices with novel sensor placements are being used. In this paper, we present a novel algorithm for breathing rate estimation from photoplethysmography (PPG) data acquired from a head-worn virtual reality mask equipped with a PPG sensor placed on the forehead of a subject. The algorithm is based on advanced signal processing and machine learning techniques and includes a novel quality assessment and motion artifacts removal procedure. The proposed algorithm is evaluated and compared to existing approaches from the related work using two separate datasets that contains data from a total of 37 subjects overall. Numerous experiments show that the proposed algorithm outperforms the compared algorithms, achieving a mean absolute error of 1.38 breaths per minute and a Pearson's correlation coefficient of 0.86. These results indicate that reliable estimation of breathing rate is possible based on PPG data acquired from a head-worn device. [ABSTRACT FROM AUTHOR]

The article focuses on the study to assess different high-frequency (HF) band power calculations based on different frequency bandwidth values, and compare them with the time domain the root mean square of successive RR differences (RMSSD) value in preterm infants. It mentions that the RMSSD was highly and positively correlated with frequency bands that were based on each preterms BR range, or on a constant frequency with band limits of 0.4–2 Hz.

The characterization of breathing dynamics provides researchers and clinicians the ability to differentiate respiratory compensation, impairment, disease progression, ventilator assistance, and the onset of respiratory failure. However, within many sub-fields of respiratory physiology, we still have challenges identifying changes within the breathing dynamics and critical respiratory states. We discuss one fundamental modeling of breathing and how modeling imprecise assumptions decades ago regarding breathing are still propagating into our quantitative analysis today, limiting our characterization and modeling of breathing. The assumption that breathing is a continuous sinusoidal wave that can consist of a single frequency which is composed of a stationary time-invariant process has limited our expanded discussion of breathing dynamics, modeling, functional testings, and metrics. Therefore, we address major misnomers regarding breathing dynamics, specifically rate, rhythm, frequency, and period. We demonstrate how these misnomers impact the characterization and modeling through the force equations that are linked to the Work of Breathing (WoB) and our interpretation of breathing dynamics through the fundamental models and create possible erroneous evaluations of work of breathing. This discussion and simplified non-periodic WoB models ultimately sets the foundation for improved quantitative approaches needed to further our understanding of breathing dynamics, compensation, and adaptation. [ABSTRACT FROM AUTHOR]

Thermal imaging-based techniques have recently emerged as a potential solution for the contactless measurement of breathing rate. However, these techniques were confined to detecting a single person’s breathing rate. In this paper, the simultaneous breathing monitoring of multiple persons is proposed by utilizing the cross-spectral visible-thermal image sequences. A cross-spectral multiple tracking-by-detection approach is used to detect multiple ROI in the visible spectrum and map the corresponding ROI in the thermal spectrum to extract the breathing signal. The linear coordinated mapping is performed by the use of a transformation matrix obtained from cross-spectral image registration. The results have been compared with the contact-based respiration belt for validation. The mean absolute error between estimated and belt-based measurements reached approximately 0.1 bpm (breaths per minute), 0.6 bpm, and 2.8 bpm for single, two, and three persons, respectively. The proposed method has a significant agreement and might be useful as a single measurement unit for contactless detection in the medical care centers, quarantine centers, and influenza screening at the airport. [ABSTRACT FROM AUTHOR]

We hypothesize that recent (2017) changes in competition rules significantly affected kinematic (mechanical activity) and physiological (cardiopulmonary indices and lactate concentration) profile of the taekwondo combat, which is currently becoming more demanding in terms of exercise intensity and fatigue. Twenty two male and female elite taekwondo athletes were followed up for over 2 years. In total, 258 real tournament combats were included in the analysis (133 in old rules and 125 in new rules). Kinematic and physiological characteristics were recorded using a biomonitor (BioHarness 3; Zephyr Technologies) worn by athletes during tournaments. Blood samples were drawn after each combat for lactate concentration. Our research showed that recent amendments of competition rules were linked to a noticeable shift toward higher kinematic output and, consequently, increased physiological response. Significant increments in kinematic variables (3-8%), heart rate (HR) (1.5-1.8%), energy expenditure (EE) (3-5%), overall physiological load (2-4%), and lactate concentration (~15% immediately after exercise and ~25% in recovery) suggest that new rules are more demanding, although the statistical effect size is only small or moderate. In conclusion, after competition rule changes, there has been a shift in taekwondo combat profile toward greater body movement dynamics, higher intensity, and greater postexercise fatigue. The values of key indices of exercise response (mechanical activity, HR, EE, and lactate concentration) during tournaments are near or exceeding the maximum attained during progressive exercise until exhaustion. Therefore, more focus is needed on taekwondo-specific high-intensity training and post-combat recovery to adapt athletes to increased competition requirements. [ABSTRACT FROM AUTHOR]

Breathing rate (BR) is one of the vital signs used in physiological monitoring. Conventional BR monitoring requires attaching wired canula/thermistor on the buco-nasal area to measure air-flow, inducing discomfort to the subject. Abdominal/thoracic belts are also used to detect breathing movements whereas esophageal pressure is the gold standard to measure breathing effort. In this paper, we aim to validate the consistency of using only bed-sheet pressure sensors to monitor the BR in healthy adults. We propose a method and demonstrate that it could be used interchangeably with respiratory belts which were approved for medical use by the American Association of Sleep Medicine (AASM). We build a ten-sinusoidal model-based extended Kalman Filter to adaptively estimate the breathing movements’ signal from the body pressure distribution data. The model is posture-specific, I.e., parameters are optimized based on the detected posture. An artificial neural network (ANN) model was used to detect four bed postures to perform the kalman filter parameters’ optimization step. The BRs of 12 healthy adults are recorded using the pressure mattress and a reference respiratory belt. To validate the method as a surrogate measure, a Bland-Altman (BA) analysis was performed on both pressure and belt data, and the linear relationship is evaluated using Pearson Correlation Coefficient (PCC). Interestingly, a high inter-rater agreement, an average maximum difference of 1.93 Breaths Per Minute (BrPM), a confidence interval of 95%, along with a strong linear relationship of 95.8% on average between the two methods were interestingly obtained. The presented results show the suitability of the proposed solution in medical applications requiring respiration monitoring. [ABSTRACT FROM AUTHOR]

To understand the growing needs of an aging human population, there is demand for scalable and reproducible approaches to study animal models of aging and to test novel therapeutic interventions. We investigated the sensitivity and utility of a continuous monitoring platform and its digital biomarkers (motion, breathing rate, and wheel running) to evaluate behavioral and physiological differences between "young" (12 weeks) and "old" (23 months) male C57BL/6J mice with or without running wheels in the home cage. Compared to young mice, old mice showed marked reductions in motion and breathing rate, as well as altered circadian rhythms. Mice without running wheels possessed lower breathing rates compared to their counterparts with running wheels. Digital biomarkers showed age-dependent changes in response to routine procedures (cage changes and blood sampling) and alterations in subjects that unexpectedly reached endpoint. Continuous collection of digital biomarkers in the home cage can enhance current approaches by providing unbiased longitudinal monitoring for large-scale aging studies. [ABSTRACT FROM AUTHOR]

In species that are subject to brood parasitism, individuals often vary in their responses to parasitic eggs, with some rejecting the eggs while others do not. While some factors, such as host age (breeding experience), the degree of egg matching and the level of perceived risk of brood parasitism have been shown to influence host decisions, much of the variation remains unexplained. The host personality hypothesis suggests that personality traits of the host influence its response to parasitic eggs, but few studies have tested this. We investigated the relationship between two personality traits (exploration and neophobia) and a physiological trait (breathing rate) of the host, and egg-rejection behaviour in a population of Daurian redstarts Phoenicurus auroreus in northeast China. We first show that exploratory behaviour and the response to a novel object are repeatable for individual females and strongly covary, indicating distinct personality types. We then show that fast-exploring and less neophobic hosts were more likely to reject parasitic eggs than slow-exploring and more neophobic hosts. Variation in breathing rate—a measure of the stress-response—did not affect rejection behaviour. Our results demonstrate that host personality, along the bold-shy continuum, predicts the responses to parasitic eggs in Daurian redstarts, with bold hosts being more likely to reject parasitic eggs. [ABSTRACT FROM AUTHOR]

Breathing rates are often collected both in the wild and in captivity to inform on cetaceans' internal state. However, few studies have investigated the effect of various factors on this breathing rate. We investigated the variations of individual and synchronous breathing rates depending on individual features (species, sex, age), displayed behavior, social parameters (social grouping), and environmental parameters (diurnal variation, presence of enrichment, unusual events, and presence of visitors in three groups of captive odontocetes (Yangtze finless porpoises, Neophocaena asiaeorentalis asiaeorientalis, East‐Asian finless porpoises, Neophocaena asiaeorentalis sunameri, bottlenose dolphins, Tursiops truncatus). Both individual and synchronous breathing rates were the highest when animals engaged in energetic or social behaviors. Individual breathing rate decreased but synchronous breathing rate increased with the presence of enrichment. Both rates increased during unusual events (e.g., pool cleaning, presence of a diver in the pool, noise, transport) and when public was present for Yangtze finless porpoises. Finally, synchronous breathing rate increased for Yangtze finless porpoises when experiencing social separation. We suggest that individual and synchronous breathing rates are useful parameters to measure, both in wild and captive animals, to obtain information on their arousal/stress state. However, these rates should be interpreted with caution and should be used together with other parameters to allow more accurate inferences. [ABSTRACT FROM AUTHOR]

In this study, a thermal imaging instrument was used to obtain facial thermal image information which was then used to calculate the number of breaths taken. However, small movements were inevitable and the first issue addressed was the means by which image calibration and region selection was to be made. To this end, thermal image sequence data calibration was done using technology that resolved small natural deviations in the nostril area. After these problems had been solved, a Hampel filter was used to process the nostril area signals. The independent component method was used to filter the effects of non‐respiratory signals, and the least squares method was employed for smoothing. Savitzky‐Golay filtering was used to adjust the signal baseline and the processed nostril region thermal image signals were compared with standard abdominal breathing band signals. Results showed that the difference in the number of breaths per minute was less than 1.5 times. The usual normal respiratory frequency range lies within a range of 0.1–0.5Hz. In the calculation of 'coherence obtained from MVAR model', the spectral coherence analysis results showed the methods proposed in this study can substantially enhance the relevance between 0.15 and 0.2Hz. [ABSTRACT FROM AUTHOR]

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Assessments of respiratory response and animal activity are useful endpoints in drug pharmacology and safety research. We investigated whether continuous, direct monitoring of breathing rate and body motion in animals in the home cage using the Vum Digital Smart House can complement standard measurements in enabling more granular detection of the onset and severity of physiologic events related to lung injury in a well-established rodent model of paraquat (PQ) toxicity. In rats administered PQ, breathing rate was significantly elevated while body motion was significantly reduced following dosing and extending throughout the 14-day study duration for breathing rate and at least 5 days for both nighttime and daytime body motion. Time course differences in these endpoints in response to the potential ameliorative test article bardoxolone were also readily detected. More complete than standard in-life measurements, breathing rate and body motion tracked injury progression continuously over the full study time period and aligned with, and informed on interval changes in clinical pathology. In addition, breathing rates correlated with terminal pathology measurements, such as normalized lung weights and histologic alveolar damage and edema. This study is a preliminary evaluation of the technology; our results demonstrate that continuously measured breathing rate and body motion served as physiologically relevant readouts to assess lung injury progression and drug response in a respiratory injury animal model. [ABSTRACT FROM AUTHOR]

Remote sensing of life detection or a non-contact monitor of vital signals is an important application for Ultra-wideband (UWB) radar, such as health monitoring of a vehicle driver. Using the UWB radar to detect physiological signals of a dynamic human, three kind movement features (body motion, breathing, and heartbeat) must be considered generally to be extracted from echo pulses. Usually, moving body echo signal is much larger than other twos, which will cause signal interference and interaction problems. Meanwhile, since moving body causes breathing and heartbeat to act in dynamic spatial position, conventional fixed feature with maximum spectrum or local peak spectrum detection methods are not likely to find those movable physiological active features. Thus, how to reduce physiological feature bias from body motion, and efficiently obtain valuable physiological information are problems. To solve these problems, a novel multi-feature alignment (MFA) two-layer EEMD method is proposed. The proposed method simultaneously detects breathing and heartbeat information from a slightly swinging human (driver). The simulation and experiment results show the proposed method can effectively and reliably evaluate breathing and heart rates in static or/and dynamic body situation, both in laboratory and car. [ABSTRACT FROM AUTHOR]

Recently, noninvasive vital signs detection is widely used in through the wall (TTW) human detection and healthcare monitoring. In indoors and TTW environments, low signal to noise ratio make the detection of the weak vital signs difficult, and causes errors in the estimation of heartbeat, respiration rate. In this paper, different techniques are applied to the signal collected using a M-sequence ultra wideband sensor from the human subject, in order to suppress the clutter, detect the target range and estimate the heartbeat and respiration rate. First, moving average method is used to remove the stationary clutter. Next, Singular Value Decomposition (SVD) is applied to cancel the non-stationary clutter. Then, variance analysis is applied to estimate the range; when range has been detected, one dimensional Singular Spectrum Analysis (SSA) is applied to the range bin containing the subject data. It is observed that SSA completely succeeds to decompose the signal into heartbeat and respiration signal and a collection of unwanted signals as clutter, noise, and harmonics. Finally, Fast Fourier transform (FFT) is used for the estimation of the heartbeat and respiration rate. Comparison results show that the proposed algorithm outperforms a reference SVD based algorithms for heartbeat and respiration rate estimation. [ABSTRACT FROM AUTHOR]

Objective: The objective of this paper is to obtain accurate estimation of breathing rate (BR), using only the electrocardiogram (ECG) or the photoplethysmogram (PPG) signals, to avoid wearing cumbersome and uncomfortable sensors for direct measurements. Methods: Several respiration waveforms are derived from ECG or PPG signals based on amplitude, frequency, and baseline wander modulations. It is, however, difficult to determine their optimal combination for BR estimation due to the noise and patient specificity. We first propose to quantify the quality of modulation waveforms using respiratory quality indices (RQIs). We then present two methods: the first automatically selects the modulation signal with highest RQI for BR estimation, and the second tracks the respiration signal using the Kalman smoother to fuse modulation signals with highest RQI. Results: These two methods are evaluated on two independent datasets, one benchmark database (DB) with immobilized patients recordings and the second with those performing daily activities. Our results outperform existing methods in the literature in both the cases. Conclusion: Experimental results show that the RQIs coupled with a fusion algorithm increases the accuracy for BR estimations in dealing with derived modulation signals. Significance: This work describes a robust Kalman Smoother method applicable in multiple clinical contexts to improve breathing rate estimation from data fusion. [ABSTRACT FROM AUTHOR]

Measuring a breathing rate has multiple clinical applications from early detection of diseases to monitoring of patients in a critical condition. Several techniques have been introduced to measure the breathing rate. The conventional breathing rate monitoring systems are contact-based and expensive, and sometimes their presence will interfere with the normal breathing system and thus produce an incorrect reading. Furthermore, they are not used in case a patient skin is sensitive (or burned skin). This paper presents a contact-free breathing rate measurement using video analysis. To accomplish this, we introduced two algorithms. The first one is approximating chest regions from face detection algorithm, and the second one is using peak-to-peak measurement to calculate a breathing rate of a person on the incoming signal. We tested the proposed algorithm on 10 subjects, and the result shows an overall 11.61% increase in accuracy (with 84.66% average accuracy) compared with FFT-based implementation. Furthermore, the time complexity of the proposed breathing rate measurement method is O(logn), whereas FFT implementation is O(nlogn). Thus, the proposed system is promising that it can be used as a standalone system or as a component of another application. [ABSTRACT FROM AUTHOR]

Breathing rate (BR) is a significant bio marker that provides both prognostic and diagnostic information for monitoring physiological condition. In addition to vital bio markers, such as blood oxygen saturation and pulse rate, BR can be extracted from non-invasive and wearable pulse oximeter based photoplethysmogram (PPG). Empirical mode decomposition (EMD) and its noise-assisted variants are widely used for decomposing non-linear and non-stationary signals. In this work, the effect of all variants of EMD in extracting BR from PPG has been investigated. We have used an EMD family PCA based hybrid model in extracting BR from PPG, which is a natural extension of our previously developed ensemble EMD (EEMD) PCA hybrid model. The performance of each model has been tested using two different datasets: MIMIC and Capnobase. Median absolute error varied from 0 to 5.03 and from 2.47 to 10.55 breaths/min for MIMIC and Capnobase dataset, respectively. Among all the EMD variants, EEMD-PCA and improved complete EEMD with adaptive noise (ICEEMDAN) PCA hybrid model present better performance for both datasets. This is the first study to compare EMD variants performance for decomposing real world signal and determine that from current methods, ICEEMDAN and EEMD are optimal for estimating BR from PPG. [ABSTRACT FROM AUTHOR]

Ultra-wideband (UWB) radar is an important remote sensing tool of life detection or a non-contact monitor of the vital signals. By processing the received UWB pulse echoes reflected from the body, different signals corresponding to heart activity and breathing, corrupted by body motion and the environment noise, are wanted to be separated clearly. However, the heartbeat signal is so tiny that it is covered by breathing harmonics and clutters. At the same time, since the frequencies of the vital signals are very close, usually around 1 Hz, it is difficult to apply an ordinary frequency filter to separate them apart. This problem induces that the vital signal detection method, usually, only detects the large breath signal, not the heartbeat signal. To solve this problem, a novel method is provided, in this paper, to extract the heartbeat and the breath information simultaneously. The method uses the feature time index with the first valley peak of the energy function of intrinsic mode functions (FVPIEF) calculated by pseudo bi-dimension ensemble empirical mode decomposition method and extracts the vital signals by the ensemble empirical mode decomposition (EEMD). Both simulation and experiment results evidently show that the proposed FVPIEF based two-layer EEMD method is effective for separating the small heartbeat signal from the large breath signal and significantly improves the evaluation of heart and breathing rates in both hold-breathing and breathing conditions. [ABSTRACT FROM AUTHOR]

Wireless body sensors are increasingly used by clinicians and researchers in a wide range of applications, such as sports, space engineering, and medicine. Monitoring vital signs in real time can dramatically increase diagnosis accuracy and enable automatic curing procedures, e.g., detect and stop epilepsy or narcolepsy seizures. Breathing parameters are critical in oxygen therapy, hospital, and ambulatory monitoring, while the assessment of cough severity is essential when dealing with several diseases, such as chronic obstructive pulmonary disease. In this paper, a low-power wireless respiratory monitoring system with cough detection is proposed to measure the breathing rate and the frequency of coughing. This system uses wearable wireless multimodal patch sensors, designed using off-the-shelf components. These wearable sensors use a low-power nine-axis inertial measurement unit to quantify the respiratory movement and a MEMs microphone to record audio signals. Data processing and fusion algorithms are used to calculate the respiratory frequency and the coughing events. The architecture of each wireless patch-sensor is presented. In fact, the results show that the small $26.67 \times 65.53$ mm2 patch-sensor consumes around 12–16.2 mA and can last at least 6 h with a miniature 100-mA lithium ion battery. The data processing algorithms, the acquisition, and wireless communication units are described. The proposed network performance is presented for experimental tests with a freely behaving user in parallel with the gold standard respiratory inductance plethysmography. [ABSTRACT FROM AUTHOR]

We develop and evaluate a respiratory rate (RR) estimation algorithm that utilizes data from the pressure-sensitive mat (PSM) technology for continuous patient monitoring in neonatal intensive care units. An analysis of the random effect of drift and systematic effect of creep in the PSM data is presented, showing that these are essentially dependent on the applied load and contact surface. Uncertainty measurements are pivotal when estimating physiologic parameters. The standard uncertainty in the PSM data is here represented by the percent drift. Next, we evaluate the applicability of the PSM technology to estimate RR in neonatal patient simulator trials under five mixed effects including internally and externally induced motion, mattress type, grunting, laying position, and different breathing rates. We analyze the limits of agreement on the mixed effects model to derive the uncertainty in the estimated RR obtained through two estimation techniques. In comparison with the gold standard RR values, we achieved a mean bias of 0.56 breaths per minute (bpm) with an error bounded by a 95% confidence interval of [−2.26, 3.37] bpm. These results meet the clinical accuracy requirements of RR within ±5 bpm. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnoea is one of the most common sleep disorders, which is characterized by the repetitive cessations of respiratory flow during sleep. This paper describes an alternative method to detect sleep parameters by measuring a variation in the earth’s magnetic field. A magnetometer sensor, laid onto the body, detects millimeter night-time breathing movements by measuring the change in the magnetic vectors. A soft, non-invasive wearable sensor is designed to be printed on a miniature printed circuit board, packaged in a minimal invasive board, which includes a wireless Bluetooth low-energy (BLE) module and a low-power microcontroller. A very low power consumption is achieved by applying a smart processing algorithm, which is also described. The algorithm delivers respiration rate, apnoea time, and time in movement. A comparison with a well-known air-flow sensor is also discussed. [ABSTRACT FROM PUBLISHER]

The study focuses on psychological and physical effects of stress while performing community garden activities of various intensity levels. The aim of this study was to determine the psychological and physical effects in adults with (case group) and without (control group) mental disabilities. Salivary α-amylase (sAA) levels and the stress response scale (SRS-18) were used for the psychological analysis (n = 42). For physical assessment (n = 13), electrocardiogram (ECG), surface electromyogram (sEMG), and respiration rate were continuously measured while performing the activities using a multichannel telemetry system. The results showed that following the activities, the case group exhibited decreasing sAA levels while control group exhibited increasing sAA levels. However, both groups exhibited lower SRS-18 results following the activities. Compared with the control group, the case group had a significantly lower increase in the ratio of the heart rate (IRHR) (5.5%) during low-intensity work (filling pots with soil), but a significantly higher IRHR (16.7%) during high-intensity work (turning over soil). The case group experienced significantly higher levels of fatigue during high-intensity work (digging) than during the rest condition. These findings indicate that appropriate workload allocation, according to health, is necessary in the community garden setting because reducing the intensity of work assignments for people with mental disabilities will reduce their physical stress. [ABSTRACT FROM AUTHOR]

A novel quantitative method for coding epochs of active and quiet sleep in infants using respiration is reported. The approach uses the variance of the instantaneous breathing rate within brief epochs of sleep. Variances are normalized within subject by dividing by the 75th percentile variance across epochs. Then, a normalized variance active sleep threshold of 0.29 was determined to produce the highest concordance with a method based on visual inspection of respiratory variability (100% and 90% for quiet and active sleep, respectively). The method was independently validated by comparing to standard polysomnographic state coding (87% and 80% concordance for quiet and active sleep) as well as with behavioral state coding (92% and 78% for quiet and active sleep). Validity was also demonstrated by showing that sleep states identified by the method resulted in the expected state differences in infant heart rate variability and electrocortical activity. [ABSTRACT FROM AUTHOR]

Although there is growing evidence that breathing is modulated by various motor and cognitive activities, the nature of breathing in musical performance has been little explored. The present study examined the temporal breath pattern in piano performance, aiming to elucidate how breath timing is related to musical organization/events and performance. In the experiments, the respiration of 15 professional and amateur pianists, playing 10 music excerpts in total (from four-octave C major scale, Hanon's exercise, J. S. Bach's Invention, Mozart's Sonatas, and Debussy's Clair de lune), was monitored by capnography. The relationship between breathing and musical characteristics was analyzed. Five major results were obtained. (1) Mean breath interval was shortened for excerpts in faster tempi. (2) Fluctuation of breath intervals was reduced for the pieces for finger exercise and those in faster tempi. Pianists showing large within-trial fluctuation also exhibited large inter-excerpt difference. (3) Inter-trial consistency of the breath patterns depended on the excerpts. Consistency was generally reduced for the excerpts that could be performed mechanically (i.e., pieces for finger exercise), but interestingly, one third of the participant showed consistent patterns for the simple scale, correlated with the ascending/descending sequences. (4) Pianists tended to exhale just after the music onsets, inhale at the rests, and inhibit inhale during the slur parts. There was correlation between breathing pattern and two-voice polyphonic structure for several participants. (5) Respiratory patterns were notably different among the pianists. Every pianist showed his or her own characteristic features commonly for various musical works. These findings suggest that breathing in piano performance depends not only on musical parameters and organization written in the score but also some pianist-dependent factors which might be ingrained to individual pianists. [ABSTRACT FROM AUTHOR]

The present study determined whether EEG and/or EMG recordings could be used to reliably define activity states in the Brazilian black and white tegu lizard ( Tupinambis merianae) and then examined the interactive effects of temperature and activity states on strategies for matching O supply and demand. In a first series of experiments, the rate of oxygen consumption ( $$\dot{V}_{{{\text{O}}_{ 2} }}$$ ), breathing frequency ( f), heart rate ( f), and EEG and EMG (neck muscle) activity were measured in different sleep/wake states (sleeping, awake but quiet, alert, or moving). In general, metabolic and cardio-respiratory changes were better indictors of the transition from sleep to wake than were changes in the EEG and EMG. In a second series of experiments, the interactive effects of temperature (17, 27 and 37 °C) and activity states on f, tidal volume ( V), the fraction of oxygen extracted from the lung per breath (FI-FE), f, and the cardiac O pulse were quantified to determine the relative roles of each of these variables in accommodating changes in $$\dot{V}_{{{\text{O}}_{ 2} }}$$ . The increases in oxygen supply to meet temperature- and activity-induced increases in oxygen demand were produced almost exclusively by increases in f and f. Regression analysis showed that the effects of temperature and activity state on the relationships between f, f and $$\dot{V}_{{{\text{O}}_{ 2} }}$$ was to extend a common relationship along a single curve, rather than separate relationships for each metabolic state. For these lizards, the predictive powers of f and f were maximized when the effects of changes in temperature, digestive state and activity were pooled. However, the best r values obtained were 0.63 and 0.74 using f and f as predictors of met abolic rate, respectively. [ABSTRACT FROM AUTHOR]

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Evidence indicates that activation of the parasympathetic nervous system (PNS) suppresses physiological responses associated with motion sickness. Research also shows paced breathing increases PNS activation; the current study examines the use of paced diaphragmatic breathing (DB) training to quell motion sickness symptoms. Healthy participants (N = 60) were pre-screened for motion sickness susceptibility. Participants were then randomly assigned to either a control condition, focusing on environmental awareness, or to an experimental condition implementing paced DB. Following this, participants were exposed to a virtual reality (VR) motion sickness experience, while heart rate variability, breathing rate (RPM), and motion sickness ratings were collected. Results demonstrated participants in the DB condition had higher PNS activation and reported fewer motion sickness symptoms during the VR experience than the participants in the control condition. Results suggest that the DB protocol can be used to significantly increase PNS tone and decrease the development of motion sickness symptoms. [ABSTRACT FROM AUTHOR]

Correlations between behavioural traits constrain animals to a limited range of behavioural choices and set a limit to the available variation in behavioural phenotype of a population. These constraints are especially important during stressful situations, potentially limiting the ability to cope with stress appropriately. As yet, very little consideration has been given to a possible role for sexual selection in maintaining differences in behavioural stability within individuals: sex-specific differences in behavioural correlations have been seldom studied, especially in the wild. In this field study, we investigated associations between neophobia (latency to enter nest in the presence of a novel object), nest defence (response to a model predator) and breathing rate in response to handling in breeding pied flycatchers ( Ficedula hypoleuca). We did not find any significant difference in antipredator responses between males and females, which indicates low sexual conflict over parental care in the pied flycatcher. However, females were more neophobic than males, while males and females did not differ in their breathing rates. Further, our study demonstrated a strong positive correlation between nest defence behaviour and neophobia in male, but not in female pied flycatchers. Males that defended their nest more had lower breathing rate and higher latency to resume nestling feeding after encountering a predator decoy than males that mobbed less intensely. We found only weak evidence that nest partners might affect each other's behaviour in these contexts. [ABSTRACT FROM AUTHOR]

Background Extracting cardiorespiratory signals from non-invasive and non-contacting sensor arrangements, i.e. magnetic induction sensors, is a challenging task. The respiratory and cardiac signals are mixed on top of a large and time-varying offset and are likely to be disturbed by measurement noise. Basic filtering techniques fail to extract relevant information for monitoring purposes. Methods We present a real-time filtering system based on an adaptive Kalman filter approach that separates signal offsets, respiratory and heart signals from three different sensor channels. It continuously estimates respiration and heart rates, which are fed back into the system model to enhance performance. Sensor and system noise covariance matrices are automatically adapted to the aimed application, thus improving the signal separation capabilities. We apply the filtering to two different subjects with different heart rates and sensor properties and compare the results to the non-adaptive version of the same Kalman filter. Also, the performance, depending on the initialization of the filters, is analyzed using three different configurations ranging from best to worst case. Results Extracted data are compared with reference heart rates derived from a standard pulsephotoplethysmographic sensor and respiration rates from a flowmeter. In the worst case for one of the subjects the adaptive filter obtains mean errors (standard deviations) of -0.2 min-1 (0.3 min-1) and -0.7 bpm (1.7 bpm) (compared to -0.2 min-1 (0.4 min-1) and 42.0 bpm (6.1 bpm) for the nonadaptive filter) for respiration and heart rate, respectively. In bad conditions the heart rate is only correctly measurable when the Kalman matrices are adapted to the target sensor signals. Also, the reduced mean error between the extracted offset and the raw sensor signal shows that adapting the Kalman filter continuously improves the ability to separate the desired signals from the raw sensor data. The average total computational time needed for the Kalman filters is under 25% of the total signal length rendering it possible to perform the filtering in real-time. Conclusions It is possible to measure in real-time heart and breathing rates using an adaptive Kalman filter approach. Adapting the Kalman filter matrices improves the estimation results and makes the filter universally deployable when measuring cardiorespiratory signals. [ABSTRACT FROM AUTHOR]