Extract Heart Rate Variability Research Articles

Automatic detection of sleep apnea from a single-lead ECG signal based on spiking neural network model

BackgroundSleep apnea (SLA) is a commonly encountered sleep disorder characterized by repetitive cessation of respiration while sleeping. In the past few years, researchers have focused on developing less complex and more cost-effective diagnostic approaches for identifying SLA recipients, in contrast to the cumbersome, complicated, and expensive conventional methods. MethodThis study presents a biologically plausible learning approach of spiking neural networks (SNN) with temporal coding and a tempotron learning model for diagnosing SLA disorder using single-lead electrocardiogram (ECG) data information. The proposed framework utilizes temporal encoding and the leaky integrate and fire model to transform the ECG signal into spikes for capturing the signal's dynamic pattern nature and to simulate input response behaviors. The tempoton learning technique, a spike-based algorithm, trains the SNN model to identify SLA event patterns from encoded output spike trains. This study utilized ECG data to extract heart rate variability (HRV) and ECG-derived respiration (EDR) signals from 1-min segment data of ECG records for input to SNN model. Thirty-five recordings of both released and withheld data from the Apnea-ECG databases from Physionet have been applied to train the SNN model and validate the model's efficacy in identifying SLA occurrences. ResultsThe proposed method demonstrated substantial improvements compared to other SLA detection techniques, achieving a significant accuracy of 94.63 % for per-segment detection, along with specificity, sensitivity, F1-score and AUC values of 96.21 %, 92.04 %, 0.9285, and 0.9851 respectively. The accuracy for per-recording detection achieved 100 %, with a correlation coefficient value of 0.986. Additionally, the experiment used UCD data for validation methods, achieving an accuracy of 84.573 %. ConclusionsThese results suggest the effectiveness and accessibility of the presented approach for accurately identifying SLA cases. The suggested model enhances the performance of SLA detection when contrasted with various techniques based on feature engineering and feature learning.

Discriminatory ability of perioperative heart rate variability in predicting postoperative complications in major urologic surgery: a prospective cohort study

We aimed to determine if continuous perioperative heart rate variability (HRV) monitoring could improve risk stratification compared to a short preoperative measurement in radical cystectomy patients. Electrocardiography (ECG) recordings were collected continuously preoperatively to discharge in 83 patients. Two, 5-min ECG signal segments (preoperative and at 24-h post ECG placement) were analyzed offline to extract HRV metrics. HRV metric discriminatory ability to identify patients with 30-day postoperative complications were analyzed using receiver operating characteristics curves. Sixty participants were included for analysis of which 27 (45%) developed a complication within 30 days postoperative. HRV was reduced in patients with complications. Postoperative standard deviation NN intervals and root mean square of successive differences had area under the curves (AUC) of 0.67 (95% CI 0.54 to 0.81) and 0.68 (95% CI 0.54 to 0.82), respectively. Significant discriminatory abilities were also reported for postoperative frequency metrics of absolute low frequency (LF) [AUC = 0.65 (95% CI 0.51 to 0.79)] and high frequency (HF) powers [AUC = 0.69 (95% CI 0.55 to 0.83)] and total power [AUC = 0.66 (95% CI 0.53 to 0.80)]. Postoperative acquired HRV metrics demonstrated improved discriminatory ability. Our findings suggest that longer-term perioperative HRV monitoring presents with superior ability to stratify complication risk.

Modular Open-Core System for Collection and Near Real-Time Processing of High-Resolution Data from Wearable Sensors

Wearable devices, such as smartwatches integrating heart rate and activity sensors, have the potential to transform health monitoring by enabling continuous, near real-time data collection and analytics. In this paper, we present a novel modular architecture for collecting and end-to-end processing of high-resolution signals from wearable sensors. The system obtains minimally processed data directly from the smartwatch and further processes and analyzes the data stream without transmitting it to the device vendor cloud. The standalone operation is made possible by a software stack that provides data cleaning, extraction of physiological metrics, and standardization of the metrics to enable person-to-person and rest-to-activity comparisons. To illustrate the operation of the system, we present examples of datasets from volunteers wearing Garmin Fenix smartwatches for several weeks in free-living conditions. As collected, the datasets contain time series of each interbeat interval and the respiration rate, blood oxygen saturation, and step count every 1 min. From the high-resolution datasets, we extract heart rate variability metrics, which are a source of information about the heart’s response to external stressors. These biomarkers can be used for the early detection of a range of diseases and the assessment of physical and mental performance of the individual. The data collection and analytics system has the potential to broaden the use of smartwatches in continuous near to real-time monitoring of health and well-being.

Predicting deterioration of patients with early sepsis at the emergency department using continuous heart rate variability analysis: a model-based approach

BackgroundSepsis is a life-threatening disease with an in-hospital mortality rate of approximately 20%. Physicians at the emergency department (ED) have to estimate the risk of deterioration in the coming hours or days and decide whether the patient should be admitted to the general ward, ICU or can be discharged. Current risk stratification tools are based on measurements of vital parameters at a single timepoint. Here, we performed a time, frequency, and trend analysis on continuous electrocardiograms (ECG) at the ED to try and predict deterioration of septic patients.MethodsPatients were connected to a mobile bedside monitor that continuously recorded ECG waveforms from triage at the ED up to 48 h. Patients were post-hoc stratified into three groups depending on the development of organ dysfunction: no organ dysfunction, stable organ dysfunction or progressive organ dysfunction (i.e., deterioration). Patients with de novo organ dysfunction and those admitted to the ICU or died were also stratified to the group of progressive organ dysfunction. Heart rate variability (HRV) features over time were compared between the three groups.ResultsIn total 171 unique ED visits with suspected sepsis were included between January 2017 and December 2018. HRV features were calculated over 5-min time windows and summarized into 3-h intervals for analysis. For each interval, the mean and slope of each feature was calculated. Of all analyzed features, the average of the NN-interval, ultra-low frequency, very low frequency, low frequency and total power were different between the groups at multiple points in time.ConclusionsWe showed that continuous ECG recordings can be automatically analyzed and used to extract HRV features associated with clinical deterioration in sepsis. The predictive accuracy of our current model based on HRV features derived from the ECG only shows the potential of HRV measurements at the ED. Unlike other risk stratification tools employing multiple vital parameters this does not require manual calculation of the score and can be used on continuous data over time.Trial registration The protocol of this study is published by Quinten et al., 2017.

Linear and Non-Linear Heart Rate Variability Indexes from Heart-Induced Mechanical Signals Recorded with a Skin-Interfaced IMU.

Heart rate variability (HRV) indexes are becoming useful in various applications, from better diagnosis and prevention of diseases to predicting stress levels. Typically, HRV indexes are retrieved from the heart's electrical activity collected with an electrocardiographic signal (ECG). Heart-induced mechanical signals recorded from the body's surface can be utilized to record the mechanical activity of the heart and, in turn, extract HRV indexes from interbeat intervals (IBIs). Among others, accelerometers and gyroscopes can be used to register IBIs from precordial accelerations and chest wall angular velocities. However, unlike electrical signals, the morphology of mechanical ones is strongly affected by body posture. In this paper, we investigated the feasibility of estimating the most common linear and non-linear HRV indexes from accelerometer and gyroscope data collected with a wearable skin-interfaced Inertial Measurement Unit (IMU) positioned at the xiphoid level. Data were collected from 21 healthy volunteers assuming two common postures (i.e., seated and lying). Results show that using the gyroscope signal in the lying posture allows accurate results in estimating IBIs, thus allowing extracting of linear and non-linear HRV parameters that are not statistically significantly different from those extracted from reference ECG.

Pediatric sleep apnea: Characterization of apneic events and sleep stages using heart rate variability

Heart rate variability (HRV) is modulated by sleep stages and apneic events. Previous studies in children compared classical HRV parameters during sleep stages between obstructive sleep apnea (OSA) and controls. However, HRV-based characterization incorporating both sleep stages and apneic events has not been conducted. Furthermore, recently proposed novel HRV OSA-specific parameters have not been evaluated. Therefore, the aim of this study was to characterize and compare classic and pediatric OSA-specific HRV parameters while including both sleep stages and apneic events. A total of 1610 electrocardiograms from the Childhood Adenotonsillectomy Trial (CHAT) database were split into 10-min segments to extract HRV parameters. Segments were characterized and grouped by sleep stage (wake, W; non-rapid eye movement, NREMS; and REMS) and presence of apneic events (under 1 apneic event per segment, e/s; 1–5 e/s; 5–10 e/s; and over 10 e/s). NREMS showed significant changes in HRV parameters as apneic event frequency increased, which were less marked in REMS. In both NREMS and REMS, power in BW2, a pediatric OSA-specific frequency domain, allowed for the optimal differentiation among segments. Moreover, in the absence of apneic events, another defined band, BWRes, resulted in best differentiation between sleep stages. The clinical usefulness of segment-based HRV characterization was then confirmed by two ensemble-learning models aimed at estimating apnea-hypopnea index and classifying sleep stages, respectively. We surmise that basal sympathetic activity during REMS may mask apneic events-induced sympathetic excitation, thus highlighting the importance of incorporating sleep stages as well as apneic events when evaluating HRV in pediatric OSA.

Emotional Models for the Estimation of Arousal and Pleasure in Older Adults During Balance Rehabilitation Training.

Emotional computing has been previously applied to assess physiological behavior in a wide variety of tasks and activities. This study extends for the first time the use of emotional computing in the field of balance rehabilitation training. A proof-of-concept study was conducted to assess arousal and pleasure response to a range of physical exercises from the OTAGO and HOLOBALANCE balance rehabilitation programs with varying levels of difficulty and physical demand. Eleven participants were enrolled and performed a set of exercises wearing an ECG sensor, reporting arousal and pleasure at the end of each session. A dataset of 264 unique sessions was collected and used to extract heart rate variability (HRV) features from the measured RR intervals and automatically assess user arousal and pleasure, evaluating different classification algorithms. The results suggested that assessment of both emotions is feasible, reaching an accuracy of 72% and 74% for arousal and pleasure estimation, resnectively. Clinical Relevance- Arousal and pleasure are clinically useful indicators of patient's experience and engagement while performing balance rehabilitation exercises with novel sensing technologies and monitoring platforms.

Measuring Heart Rate Variability Using Facial Video.

Heart Rate Variability (HRV) has become an important risk assessment tool when diagnosing illnesses related to heart health. HRV is typically measured with an electrocardiogram; however, there are multiple studies that use Photoplethysmography (PPG) instead. Measuring HRV with video is beneficial as a non-invasive, hands-free alternative and represents a more accessible approach. We developed a methodology to extract HRV from video based on face detection algorithms and color augmentation. We applied this methodology to 45 samples. Signals obtained from PPG and video recorded an average mean error of less than 1 bpm when measuring the heart rate of all subjects. Furthermore, utilizing PPG and video, we computed 61 variables related to HRV. We compared each of them with three correlation metrics (i.e., Kendall, Pearson, and Spearman), adjusting them for multiple comparisons with the Benjamini–Hochberg method to control the false discovery rate and to retrieve the q-value when considering statistical significance lower than 0.5. Using these methods, we found significant correlations for 38 variables (e.g., Heart Rate, 0.991; Mean NN Interval, 0.990; and NN Interval Count, 0.955) using time-domain, frequency-domain, and non-linear methods.

Analysis of physiological and non-contact signals to evaluate the emotional component in consumer preferences.

Emotions are an essential drive in decision making and may influence consumer preference. In this study we assessed the influence of brands in product preference after tasting 2 soft-drinks (Coca Cola vs. Cola beverage), by using physiological measurements, namely the skin conductance, the respiratory rate and heart rate variability (HRV) extracted using contactless sensors. The experimental protocol enrolled twenty-five young subjects which were asked to taste 2 soft drinks in random order, without knowing the brand (blind condition) and then knowing the brand (not blind condition). After each phase the subject was asked to choose the preferred beverage. Our main hypothesis is that if the subject knows the brand there is an arousal effect, independently from the absolute appreciation of the product. In order to evaluate the emotional components, the skin conductance, respiratory and Video-Photoplethysmographic (PPG) signals were continuously recorded throughout the experiment. The Video-PPG was then processed to extract HRV parameters. We observed that the arousal levels changed among beverages and conditions, going from higher arousal for Coca-Cola in the blind condition, to higher arousal for Cola in the not blind condition. Moreover, 44% of the subjects changed their preference when the brand was uncovered: from blind to not blind conditions, 6 subjects went from Cola to Coca-Cola as preferred drink and 5 went from Coca-Cola to Cola. Opposite results were found for the two beverages when comparing the physiological response when the beverage was/was not preferred. Finally, differences were found also between consumers and not consumers of Coca-Cola and the blind/not blind comparison. We conclude that the brand is a fundamental element in a request for choice and it can affect the first emotional response of a subject.

Acoustic Sensing as a Novel Wearable Approach for Heart Rate Variability Monitoring at the Wrist

Heart rate variability (HRV) is an important physiological parameter to assess the status of the autonomic nervous system (ANS) and diagnose cardiovascular diseases (CVDs). HRV is generally extracted from the cardiac activity monitored at the chest using electrocardiography (ECG). This article presents a novel algorithm to accurately extract HRV, from signals obtained using an innovative wearable approach based on acoustic sensing of cardiac activity at the wrist. The proposed methodology is based on the relative energy method, which utilizes the relative information between short and long-term energies of the acoustic signal to enhance the S1’s characteristics in comparison to other signal transitions. The algorithm was tested on a dataset consisting of acoustic recordings of 5-min duration obtained from 12 subjects. For the instantaneous heart rate (IHR) determination, 98.50% and 98.96% of the total samples achieved a relative error of less than ±5% when compared to ECG and photoplethysmography (PPG) signals, respectively. The comparison of the HRV parameters in the time-domain and the frequency-domain, with the reference signals, also demonstrated a strong statistical agreement and correlation of close to one, hence proving the feasibility of extracting the HRV from acoustic signals recorded at the wrist.

Hedonic Responses to Touch are Modulated by the Perceived Attractiveness of the Caresser

Previous research has shown that a specific type of C fiber, the C tactile afferents, are involved in detecting gentle, dynamic tactile stimuli on the skin, giving rise to affective responses in the central nervous system. Despite building on such bottom-up information flow, the hedonic perception and the physiological consequences of affective touch are influenced by various sources of top-down information. In the present study we investigated how perception of affective touch is influenced by the attractiveness of hypothetical caressers. Participants were stroked on the arm and the palm while looking at photos of high attractive and low attractive opposite-gender faces, and were instructed to imagine those people as the caressers. In a control condition no photo was paired with the touch. The stroking stimulation was delivered with a soft brush either on the forearm or on the palm, and either with a slower or faster speed. Participants rated the pleasantness of each stimulation, while electrocardiographic recordings were made to extract heart rate variability data. Results showed that participants preferred touch stimuli paired with high attractive faces; they also preferred palm stroking and slower stroking speed. Like subjective pleasantness ratings, heart rate variability responses to affective touch (slow) were higher for high attractive than for low attractive caressers, but were not selective for arm or palm stroking. Overall, the present study confirms that contextual social information plays a major role in affective touch experiences, influencing not only the hedonic quality of the experience but also the physiological state of the body.

Continuous m-Health Data Authentication Using Wavelet Decomposition for Feature Extraction.

The World Health Organization (WHO) in 2016 considered m-health as: “the use of mobile wireless technologies including smart devices such as smartphones and smartwatches for public health”. WHO emphasizes the potential of this technology to increase its use in accessing health information and services as well as promoting positive changes in health behaviours and overall management of diseases. In this regard, the capability of smartphones and smartwatches for m-health monitoring through the collection of patient data remotely, has become an important component in m-health system. It is important that the integrity of the data collected is verified continuously through data authentication before storage. In this research work, we extracted heart rate variability (HRV) and decomposed the signals into sub-bands of detail and approximation coefficients. A comparison analysis is done after the classification of the extracted features to select the best sub-bands. An architectural framework and a used case for m-health data authentication is carried out using two sub-bands with the best performance from the HRV decomposition using 30 subjects’ data. The best sub-band achieved an equal error rate (EER) of 12.42%.

A Field Study to Capture Events of Interest (EoI) from Living Labs Using Wearables for Spatiotemporal Monitoring Towards a Framework of Smart Health (sHealth).

Everyday wearables such as smartwatches or smart bands can play a pivotal role in the field of fitness and wellness and hold the prospect to be used for early disease detection and monitoring towards Smart Health (sHealth). One of the challenges is the extraction of reliable biomarkers from data collected using these devices in the real world (Living Labs). In this yearlong field study, we collected the nocturnal instantaneous heart rate from 9 participants using wrist-worn commercial smart bands and extracted heart rate variability features (HRV). In addition, we measured core body temperature using our custom-designed flexible Inkjet-Printed (IJP) temperature sensor and SpO2 with a finger pulse oximeter. The core body temperature along with user-reported symptoms have been used for automated spatiotemporal monitoring of flu symptoms severity in real-time. The extracted HRV feature values are within the 95% confidence interval of normative values and shows an anticipated trend for gender and age. The resulting dataset from this study is a novel addition and may be used for future investigations.Clinical Relevance- The findings of this study shows usability of wearables in detection and monitoring of diseases such as obstructive sleep apnea reducing the prevalence of undiagnosed cases. This framework also has potentials to monitor outbreaks of flu and other diseases with spatiotemporal distribution.

Identification of Stress State for Drivers Under Different GPS Navigation Modes

It is commonly known that Global Positioning System (GPS) can alleviate travelling difficulties of automobile drivers, and generally we hold the view that it reduces the driver's stress when they are in unfamiliar road conditions. In this research, an in-laboratory experiment and an in-car experiment are conducted to find out whether GPS instructions can reduce or may induce additional mental stress of drivers. Electrocardiography (ECG) signals are collected in the experiments and the extracted heart rate variability (HRV) features are used for analysis. Three binary classifiers, specifically Support Vector Machine, k-Nearest Neighbor (k-NN) and Random Forest, are trained based on the data collected in the in-laboratory experiment, where the stress state is elicited by the Stroop color word Test. The k-NN classifier outperforms the other two classifiers, and thus is applied to the data collected in the in-car experiment, to identify drivers' stress state under different driving events, such as waiting for traffic lights, turning, under GPS instructions, and traffic conditions like overtaking, or changing lanes. During each event, whether the driver is in stress or relaxed state for each time instant is predicted based on the trained classifier. The percentages of time that the driver is in stress state for each type of events are computed. It shows that GPS instructions cause the second largest time-percentage of stress state, lower than that caused by the turning event, but higher than that caused by the events of waiting for traffic lights and other traffic conditions.

Can a Smartband be Used for Continuous Implicit Authentication in Real Life

The use of cloud services that process privacy-sensitive information such as digital banking, pervasive healthcare, smart home applications requires an implicit continuous authentication solution, which will make these systems less vulnerable to the spoofing attacks. Physiological signals can be used for continuous authentication due to their uniqueness. Ubiquitous wrist-worn wearable devices are equipped with photoplethysmogram sensors, which enable us to extract heart rate variability (HRV) features. In this study, we show that these devices can be used for continuous physiological authentication for enhancing the security of the cloud, edge services, and IoT devices. A system that is suitable for the smartband framework comes with new challenges such as relatively low signal quality and artifacts due to placement, which were not encountered in full lead electrocardiogram systems. After the artifact removal, cleaned physiological signals are fed to the machine learning algorithms. In order to train our machine learning models, we collected physiological data using off-the-shelf smartbands and smartwatches in a real-life event. By applying a minimum quality threshold, we achieved a 3.96% Equal Error Rate. Performance evaluation shows that HRV is a strong candidate for continuous unobtrusive implicit physiological authentication.

Simulation and in vivo investigation of light-emitting diode, near infrared Gaussian beam profiles

Near infrared spectroscopy is an optical imaging technique which offers a non-invasive, portable, and low-cost method for continuously measuring the oxygenation of tissues. In particular, it can provide the brain activation through measuring the blood oxygenation and blood volume in the cortex. Understanding and then improving the spatial and depth sensitivity of near infrared spectroscopy measurements to brain tissue are essential for designing experiments as well as interpreting research findings. In this study, we investigate the effect of applying two common light beam profiles including Uniform and Gaussian on the penetration depth of an LED-based near infrared spectroscopy. In this regard, two Gaussian profiles were produced by adjusting plano-convex and bi-convex lenses and the Uniform profile was provided by applying a flat lens. Two experiments were conducted in this study. First, a simulation experiment was carried out based on scanning the intra space of a liquid phantom by using static and pulsating absorbers to compare the penetration depth of the configurations applied on the LED-based near infrared spectroscopy with that of a laser-based near infrared spectroscopy. Second, to show the feasibility of the best proposed configuration applied, an in vivo experiment of stress assessment has been performed and its results have been compared with that results obtained by laser one. The results showed that the LED-based near infrared spectroscopy equipped with bi-convex lens provides a penetration depth and hence quality measurements of near infrared spectroscopy and its extracted heart rate variability signals as well as laser-based near infrared spectroscopy especially in the application of stress assessment.

Heart Rate Variability as Indicator of Clinical State in Depression.

Background: Depression is a severe disease with great burdens for the affected individuals and public health care systems. Autonomic nervous system (ANS) dysfunction indexed by measures of heart rate variability (HRV) has repeatedly been associated with depression. However, HRV parameters are subject to a wide range of multi-factorial influences and underlying mechanisms in depression are still unclear. HRV parameters have been proposed to be promising candidates for diagnostic or predictive bio-markers for depression but necessary longitudinal design studies investigating the relationship between HRV and depression are scarce.Methods: The sample in this study consisted of 62 depressive individuals without antidepressant medication prior to assessment and 65 healthy controls. Fifteen minute blocks of resting ECGs were recorded 1–2 days before onset of antidepressant treatment and 2 weeks thereafter. The ECGs were pre-processed to extract inter-beat-intervals. Linear and non-linear methods were used to extract HRV parameters. ANOVAS were performed to investigate group differences between depressive patients and healthy controls. Associations between the change in severity of depression and HRV parameters were assessed in a repeated measurements design.Results: Analyses revealed HRV parameter differences between the groups of depressive patients and healthy controls at baseline. Further results show differences in HRV parameters within subjects after 2 weeks of antidepressant treatment. Change in HRV parameter values correlated with changes in symptom severity of depression.Discussion: The current results provide further insight into the relationship between HRV parameters and depression. This may help to underpin utilization of HRV parameters are bio-maker for disease state in depression. Results are discussed within a theoretical framework to link arousal and ANS regulation in depression.

Nonlinear Heart Rate Variability based artificial intelligence in lung cancer prediction

Lung cancer is uncontrolled growth of cells that occurs due to exposure to smoke, radiation and chemicals, which causes chronic stress and associated with impaired autonomic nervous system. Nonlinear heart rate variability (HRV) analysis has been suggested to uncover the performance status of lung cancer subjects and distinguish them from healthy controls. The present work obtained tachogram from recorded electrocardiogram of 104 lung cancer subjects and 30 healthy controls to extract HRV indices. The obtained results suggested lowered HRV (altered autonomic nervous system tone) values from Eastern Cooperative Oncology Group (ECOG) 1 to ECOG4. Subject males had higher HRV measures than their female counterparts. The HRV parameters decreased from ECOG PS of 1 to 4. Control females had higher HRV measures than control males. There was no association between age and HRV measures. Statistically, nonlinear HRV features were observed significant. ANN exhibited ECOG1 83.3%, ECOG2 50%, ECOG3 90%, ECOG4 95% and Controls 86.7%. The prediction analysis using artificial neural network (ANN) and support vector machine (SVM) scoring an accuracy of 93.09% and 100% with nonlinear HRV indices as input thus has been suggested to be a tool of prognostic importance.

A New Ka-Band Doppler Radar in Robust and Precise Cardiopulmonary Remote Sensing

In this paper, a new Ka-band Doppler radar with an ability to remotely monitor respiration rate, heartbeat rate (HR), and heart rate variability (HRV) is presented. Exploiting the advantage of high sensitivity of Ka-band radar in sensing of chest-wall movement, we make more accurate reconstruction of cardiopulmonary activities from low-quality received signal. The advantage of using Ka-band radar is obtained by paying a penalty, which is harmonic interferences that make measuring of cardiopulmonary details infeasible in lots of real scenarios. In this paper, by using a more complete model of return signal, a new routine is introduced for the robust detection of breathing and HR that extremely alleviates the problems associated with harmonic interferences. Besides, a more suitable model is proposed, which can cover time-variability cardiorespiratory features. Taking advantage of this model and based on the unconditional orthonormal representation of band-limited signals, a filter with time-varying coefficients is proposed to extract HRV. This extraction is done without resorting to tracking HR trace that makes it more precise and robust than the conventional approach. The indicated filter enables us to suggest a novel approach to remove random body motion effects. By experimental data, the applicability of the new radar for a wide range of real world scenarios is investigated, and its advantages beside conventional approaches are proved practically in different aspects.

Stage call: Cardiovascular reactivity to audition stress in musicians

Auditioning is at the very center of educational and professional life in music and is associated with significant psychophysical demands. Knowledge of how these demands affect cardiovascular responses to psychosocial pressure is essential for developing strategies to both manage stress and understand optimal performance states. To this end, we recorded the electrocardiograms (ECGs) of 16 musicians (11 violinists and 5 flutists) before and during performances in both low- and high-stress conditions: with no audience and in front of an audition panel, respectively. The analysis consisted of the detection of R-peaks in the ECGs to extract heart rate variability (HRV) from the notoriously noisy real-world ECGs. Our data analysis approach spanned both standard (temporal and spectral) and advanced (structural complexity) techniques. The complexity science approaches—namely, multiscale sample entropy and multiscale fuzzy entropy—indicated a statistically significant decrease in structural complexity in HRV from the low- to the high-stress condition and an increase in structural complexity from the pre-performance to performance period, thus confirming the complexity loss theory and a loss in degrees of freedom due to stress. Results from the spectral analyses also suggest that the stress responses in the female participants were more parasympathetically driven than those of the male participants. In conclusion, our findings suggest that interventions to manage stress are best targeted at the sensitive pre-performance period, before an audition begins.