Trends in Heart-Rate Variability Signal Analysis

Enhancing Comprehensive Assessments in Chronic Heart Failure Caused by Ischemic Heart Disease: The Diagnostic Utility of Holter ECG Parameters

Background and Objectives: Chronic heart failure (CHF) caused by ischemic heart disease (IHD) is the leading cause of death worldwide and presents significant health challenges. Effective management of IHD requires prevention, early detection, and treatment to improve patient outcomes. This study aims to expand the diagnostic utility of various 24 h Holter ECG parameters, such as T-wave alternans (TWA), late ventricular potentials (LVPs), and heart rate variability (HRV) in patients with CHF caused by IHD. Additionally, we seek to explore the association between these parameters and other comorbid conditions affecting the prognosis of CHF patients. Materials and Methods: We conducted a prospective case-control study with 150 patients divided into two subgroups: 100 patients with CHF caused by IHD, and 50 patients in the control group. Data included medical history, physical examination, laboratory tests, echocardiography, and 24 h Holter monitoring. Results: Our comparative analysis demonstrated that both TWA and LVPs were significantly higher in patients with CHF compared to the control group (p < 0.01), indicating increased myocardial electrical vulnerability in CHF patients. Both time and frequency-domain HRV parameters were significantly lower in the CHF group. However, the ratio of NN50 to the total count of NN intervals (PNN50) showed a borderline significance (p = 0.06). While the low-frequency (LF) domain was significantly lower in CHF patients, the high-frequency (HF) domain did not differ significantly between groups. Acceleration and deceleration capacities were also significantly altered in CHF patients. Categorizing CHF patients by left ventricular ejection fraction (LVEF) revealed that the mean of the 5-min normal-to-normal intervals over the complete recording (SDNN Index) was significantly higher in patients with LVEF ≥ 50% compared to those with CHF with reduced EF and CHF with mildly reduced EF (p < 0.001), whereas the other HRV parameters showed no significant differences among the groups. Conclusions: Holter ECG parameters can become a reliable tool in the assessment of patients with CHF. The integration of multiple Holter ECG parameters, such as TWA, LVPs, and HRV, can significantly enhance the diagnostic assessment of CHF caused by IHD. This comprehensive approach allows for a more nuanced understanding of the patient's condition and potential outcomes.

The Clinical Significance and Application of Heart Rate Variability in Dialysis Patients: A Narrative Review

Autonomic nervous system (ANS) dysfunction is prevalent in end-stage kidney disease (ESKD) patients, carrying significant risks for morbidity and mortality. Heart rate variability (HRV) is a simple and non-invasive method to evaluate ANS functions and predict prognoses in specific patient populations. Since there is a lack of a clear understanding of the clinical significance of HRV in predicting prognoses in ESKD patients, an updated review on this topic is urgently warranted. The clinical significance of HRV in dialysis patients includes its associations with metabolic syndrome, nutritional status, intradialytic hypotension, vascular access failure, major adverse cardiovascular events, and mortality. These findings underscore the essential role of the autonomic reserve, which might denote the elevation of ANS activity as a response to external stimulus. Patients with a higher level of sympathetic activity at the resting stage, but who are unable to adequately elevate their sympathetic activity under stress might be susceptible to a worse outcome in critical circumstances. Further applications of HRV include HRV biofeedback, risk classification, and real-time HRV monitoring. Overall, HRV is an optimal tool for predicting prognoses in dialysis patients. Further study is encouraged in order to gain a clearer understanding of the clinical significance and application of HRV, and thereby enhance the care of ESKD patients.

Pulse Wave Velocity: Methodology, Clinical Applications, and Interplay with Heart Rate Variability

Pulse wave velocity (PWV) has been established as a promising biomarker in cardiovascular diagnostics, providing deep insights into vascular health and cardiovascular risk. Defined as the velocity at which the mechanical wave propagates along the arterial wall, PWV represents a useful surrogate marker for arterial vessel stiffness. PWV has garnered clinical attention, particularly in monitoring patients suffering from vascular diseases such as hypertension and diabetes mellitus. Its utility extends to preventive cardiology, aiding in identifying and stratifying cardiovascular risk. Despite the development of various measurement techniques, direct or indirect tonometry, Doppler ultrasound, oscillometric analysis, and magnetic resonance imaging (MRI), methodological variability and lack of standardization lead to inconsistencies in PWV assessment. In addition, PWV can be estimated through surrogate parameters, such as pulse arrival or pulse transit times, although this heterogeneity limits standardization and, therefore, its clinical use. Furthermore, confounding factors, such as variations in sympathetic tone, strongly influence PWV readings, thereby necessitating careful control during assessments. The bidirectional relationship between heart rate variability (HRV) and PWV underscores the interplay between cardiac autonomic function and vascular health, suggesting that alterations in one could directly influence the other. Future research should prioritize the standardization and increase comparability of PWV measurement techniques and explore the complex physiological variables influencing PWV. Integrating multiple physiological parameters such as PWV and HRV into algorithms based on artificial intelligence holds immense promise for advancing personalized vascular health assessments and cardiovascular care.

Association between heart rate variability metrics from a smartwatch and self-reported depression and anxiety symptoms: a four-week longitudinal study

Background

Elucidating the association between heart rate variability (HRV) metrics obtained through non-invasive methods and mental health symptoms could provide an accessible approach to mental health monitoring. This study explores the correlation between HRV, estimated using photoplethysmography (PPG) signals, and self-reported symptoms of depression and anxiety.

Methods

A 4-week longitudinal study was conducted among 47 participants. Time-domain and frequency-domain HRV metrics were derived from PPG signals collected via smartwatches. Mental health symptoms were evaluated using the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) at baseline, week 2, and week 4.

Results

Among the investigated HRV metrics, RMSSD, SDNN, SDSD, LF, and the LF/HF ratio were significantly associated with the PHQ-9 score, although the number of significant correlations was relatively small. Furthermore, only SDNN, SDSD and LF showed significant correlations with the GAD-7 score. All HRV metrics showed negative correlations with self-reported clinical symptoms.

Conclusions

Our findings indicate the potential of PPG-derived HRV metrics in monitoring mental health, thereby providing a foundation for further research. Notably, parasympathetically biased HRV metrics showed weaker correlations with depression and anxiety scores. Future studies should validate these findings in clinically diagnosed patients.

Facilitating ambulatory heart rate variability analysis using accelerometry-based classifications of body position and self-reported sleep

Objective.This study aimed to examine differences in heart rate variability (HRV) across accelerometer-derived position, self-reported sleep, and different summary measures (sleep, 24 h HRV) in free-living settings using open-source methodology.Approach.HRV is a biomarker of autonomic activity. As it is strongly affected by factors such as physical behaviour, stress, and sleep, ambulatory HRV analysis is challenging. Beat-to-beat heart rate (HR) and accelerometry data were collected using single-lead electrocardiography and trunk- and thigh-worn accelerometers among 160 adults participating in the SCREENS trial. HR files were processed and analysed in the RHRV R package. Start time and duration spent in physical behaviours were extracted, and time and frequency analysis for each episode was performed. Differences in HRV estimates across activities were compared using linear mixed models adjusted for age and sex with subject ID as random effect. Next, repeated-measures Bland-Altman analysis was used to compare 24 h RMSSD estimates to HRV during self-reported sleep. Sensitivity analyses evaluated the accuracy of the methodology, and the approach of employing accelerometer-determined episodes to examine activity-independent HRV was described.Main results.HRV was estimated for 31 289 episodes in 160 individuals (53.1% female) at a mean age of 41.4 years. Significant differences in HR and most markers of HRV were found across positions [Mean differences RMSSD: Sitting (Reference) - Standing (-2.63 ms) or Lying (4.53 ms)]. Moreover, ambulatory HRV differed significantly across sleep status, and poor agreement between 24 h estimates compared to sleep HRV was detected. Sensitivity analyses confirmed that removing the first and last 30 s of accelerometry-determined HR episodes was an accurate strategy to account for orthostatic effects.Significance.Ambulatory HRV differed significantly across accelerometry-assigned positions and sleep. The proposed approach for free-living HRV analysis may be an effective strategy to remove confounding by physical activity when the aim is to monitor general autonomic stress.

Circadian changes of autonomic function in patients with zoster-associated pain: A heart rate variability analysis

OBJECTIVE

To investigate the circadian changes of the autonomic function in patients with zoster-associated pain (ZAP).

METHODS

A total of 37 patients with ZAP from April 2022 to October 2022 were enrolled as the observation group, and 37 normal volunteers at the same time were selected as the control group. All participants were required to wear a 24-h Holter, which was used to compare the heart rate variability (HRV) between the two groups. HRV analysis involved time- and frequency-domain parameters.

RESULTS

There was no statistically significant difference in general information between two groups. Patients with ZAP had an increased mean heart rate and decreased the standard deviation of normal-to-normal (SDNN) R-R interval, the root mean square of the differences (RMSSD) in successive RR interval, low frequency (LF), and high frequency (HF) compared with control groups in all periods (p < .05). The ratio of LF/HF between two groups had no significant difference (p = .245). SDNN had no significant difference between day and night in the control group (p > .05), whereas SDNN of ZAP patients in night period was reduced than that in day period (p < .001). The level of RMSSD during the day was lower than those at night in the control group (p < .05), whereas no significant difference of RMSSD between two periods was observed in patients with ZAP (p > .05).

CONCLUSION

The results of this study indicated that ZAP contributes to the decline of autonomic nervous system (ANS) function, especially parasympathetic components. The patients with ZAP lost parasympathetic advantage and had a worse ANS during the night.

Examination of Cardiac Activity with ECG Monitoring Using Heart Rate Variability Methods

The paper presents a system for analyzing cardiac activity with the possibility of continuous and remote monitoring. The created sensor mobile device monitors heart activity by means of the convenient and imperceptible registration of cardiac signals. At the same time, the behavior of the human body is also monitored through the accelerometer and gyroscope built into the device, thanks to which it is possible to signal in the event of loss of consciousness or fall (in patients with syncope). Conducting real-time cardio monitoring and the analysis of recordings using various mathematical methods (linear, non-linear, and graphical) enables the research, accurate diagnosis, timely assistance, and correct treatment of cardiovascular diseases. The paper examines the recordings of patients diagnosed with arrhythmia and syncope recorded by electrocardiography (ECG) sensors in real conditions. The obtained results are subjected to statistical analysis to determine the accuracy and significance of the obtained results. The studies show significant deviations in the patients with arrhythmia and syncope regarding the obtained values of the studied parameters of heart rate variability (HRV) from the accepted normal values (for example, the root mean square of successive differences between normal heartbeats (RMSSD) in healthy individuals is 24.02 ms, while, in patients with arrhythmia (6.09 ms) and syncope (5.21 ms), it is much lower). The obtained quantitative and graphic results identify some possible abnormalities and demonstrate disorders regarding the activity of the autonomic nervous system, which is directly related to the work of the heart.

Analyzing population-level trials as N-of-1 trials: An application to gait

Studying individual causal effects of health interventions is important whenever intervention effects are heterogeneous between study participants. Conducting N-of-1 trials, which are single-person randomized controlled trials, is the gold standard for their analysis. As an alternative method, we propose to re-analyze existing population-level studies as N-of-1 trials, and use gait as a use case for illustration. Gait data were collected from 16 young and healthy participants under fatigued and non-fatigued, as well as under single-task (only walking) and dual-task (walking while performing a cognitive task) conditions. As a reference to the N-of-1 trials approach, we first computed standard population-level ANOVA models to evaluate differences in gait parameters (stride length and stride time) across conditions. Then, we estimated the effect of the interventions on gait parameters on the individual level through Bayesian repeated-measures models, viewing each participant as their own trial, and compared the results. The results illustrated that while few overall population-level effects were visible, individual-level analyses revealed differences between participants. Baseline values of the gait parameters varied largely among all participants, and the effects of fatigue and cognitive task were also heterogeneous, with some individuals showing effects in opposite directions. These differences between population-level and individual-level analyses were more pronounced for the fatigue intervention compared to the cognitive task intervention. Following our empirical analysis, we discuss re-analyzing population studies through the lens of N-of-1 trials more generally and highlight important considerations and requirements. Our work encourages future studies to investigate individual effects using population-level data.

Positive affect and heart rate variability: a dynamic analysis

Traditional survey methods can provide noisy data arising from recall, memory and other biases. Technological advances (particularly in neuroscience) are opening new ways of monitoring physiological processes through non-intrusive means. Such dense continuous data provide new and fruitful avenues for complementing self-reported data with a better understanding of human dynamics and human interactions. In this study, we use a survey to collect positive affect (feelings) data from more than 300 individuals over a period of 24 h, and at the same time, map their core activities (5000 recorded activities in total) with measurements of their heart rate variability (HRV). Our results indicate a robust correlation between the HRV measurements and self-reported affect. By drawing on the neuroscience and wellbeing literature we show that dynamic HRV results are what we expect for positive affect, particularly when performing activities like sleep, travel, work, exercise and eating. This research provides new insights into how to collect HRV data, model and interpret it.

Obstructive sleep apnea heterogeneity and autonomic function: a role for heart rate variability in therapy selection and efficacy monitoring

Obstructive sleep apnea is a highly prevalent sleep-related breathing disorder, resulting in a disturbed breathing pattern, changes in blood gases, abnormal autonomic regulation, metabolic fluctuation, poor neurocognitive performance, and increased cardiovascular risk. With broad inter-individual differences recognised in risk factors, clinical symptoms, gene expression, physiological characteristics, and health outcomes, various obstructive sleep apnea subtypes have been identified. Therapeutic efficacy and its impact on outcomes, particularly for cardiovascular consequences, may also vary depending on these features in obstructive sleep apnea. A number of interventions such as positive airway pressure therapies, oral appliance, surgical treatment, and pharmaceutical options are available in clinical practice. Selecting an effective obstructive sleep apnea treatment and therapy is a challenging medical decision due to obstructive sleep apnea heterogeneity and numerous treatment modalities. Thus, an objective marker for clinical evaluation is warranted to estimate the treatment response in patients with obstructive sleep apnea. Currently, while the Apnea-Hypopnea Index is used for severity assessment of obstructive sleep apnea and still considered a major guide to diagnosis and managements of obstructive sleep apnea, the Apnea-Hypopnea Index is not a robust marker of symptoms, function, or outcome improvement. Abnormal cardiac autonomic modulation can provide additional insight to better understand obstructive sleep apnea phenotyping. Heart rate variability is a reliable neurocardiac tool to assess altered autonomic function and can also provide cardiovascular information in obstructive sleep apnea. Beyond the Apnea-Hypopnea Index, this review aims to discuss the role of heart rate variability as an indicator and predictor of therapeutic efficacy to different modalities in order to optimise tailored treatment for obstructive sleep apnea.

The importance of personalization in high altitude protocols for hematologic and metabolic benefits in sports: A multi-dimensional N-of-1 case study

The hematologic and metabolic benefits of high altitude exposure have been extensively studied in athletes due to their promising performance enhancing effects. However, despite the increased research and development of various high altitude protocols for achieving peak performance, the reproducibility of the results at the individual level remains sparse. To systematically address this limitation and establish a more effective method to achieve consistent results at the individual level, we conducted a multi-dimensional study of one elite endurance athlete in two Phases. In Phase 1, we applied the standard protocol of LHTH (Live-High-Train-High) using a commercially available, at-home, normobaric, high altitude simulation tent under the SHTL (Sleep-High-Train-Low) model. Then, we developed the athlete's personalized protocol for peak hematologic parameters during their off-season. This protocol determined the exact total high altitude exposure time required to achieve peak hematologic parameters, which in the case of this athlete, amounted to 45 nights with approximately 8hrs per night. In Phase 2, we replicated the Phase 1 protocol during the athlete's in-season and observed the same or even higher hematologic and metabolic benefits compared to Phase 1. During both phases, we collected thousands of multi-dimensional data points to ensure that the athlete's lifestyle and environmental factors remained stable, and to increase the likelihood that physiological changes resulted primarily from the high altitude exposure. The data trends in both Phases validated that, for this athlete, hematologic measures such as red blood cell count, hematocrit, and hemoglobin, as well as electrolyte content, body weight and gut microbiome composition improved to their personal best values after a total of approximately 15 days of high altitude exposure (45 nights with roughly 8hrs per night totaling 360hrs or 15days). These improvements did not occur after the 21 days recommended by the LHTH protocol highlighting the significance of personalization in high altitude protocols that are designed for peak performance parameters. Therefore, to maximize the benefits in hematologic and other metabolic values and thus increase muscle oxygen supply and peak aerobic capacity through high altitude exposure, each athlete may require a unique total duration of high altitude exposure tailored to their individual physiology. This duration must be determined by their specific response in hematologic peaking. Therefore, initially establishing a personalized protocol for an athlete by determining their required total duration of high altitude exposure for peak hematologic values during their off-season and applying this protocol during their in-season phase may lead to more successful and reproducible benefits compared to following a generalized protocol alone.

Exercise Exertion Level Prediction Using Data from Wearable Physiologic Monitors

This study aims to develop machine learning (ML) algorithms to predict exercise exertion levels using physiological parameters collected from wearable devices. Real-time ECG, oxygen saturation, pulse rate, and revolutions per minute (RPM) data were collected at three intensity levels during a 16-minute cycling exercise. Parallel to this, throughout each exercise session, the study subjects' ratings of perceived exertion (RPE) were gathered once per minute. Each 16-minute exercise session was divided into a total of eight 2-minute windows. Each exercise window was labeled as "high exertion," or "low exertion" classes based on the self-reported RPEs. For each window, the gathered ECG data were used to derive the heart rate variability (HRV) features in the temporal and frequency domains. Additionally, each window's averaged RPMs, heart rate, and oxygen saturation levels were calculated to form all the predictive features. The minimum redundancy maximum relevance algorithm was used to choose the best predictive features. Top selected features were then used to assess the accuracy of ten ML classifiers to predict the next window's exertion level. The k-nearest neighbors (KNN) model showed the highest accuracy of 85.7% and the highest F1 score of 83%. An ensemble model showed the highest area under the curve (AUC) of 0.92. The suggested method can be used to automatically track perceived exercise exertion in real-time.

Analysis of the correlation between heart rate variability and palpitation symptoms in female patients with long COVID

Objectives

To analyze the correlation between heart rate variability (HRV) and palpitation symptoms in female patients with long COVID.

Methods

A total of 272 female healthcare workers who were infected with SARS-CoV-2 for the first time in December 2022 at Fuzhou First Hospital affiliated with Fujian Medical University, were selected as study subjects. These subjects were divided into three groups based on their symptoms: a group with palpitations (70 cases), a group without palpitations but with other symptoms (124 cases), and a group consisting of asymptomatic cases (78 cases). The study compared the general information, COMPASS-31 scores, quality of life scores, and HRV parameters among the three groups. Furthermore, it analyzed the factors influencing palpitation symptoms in female patients with long COVID.

Results

Compared to the other two groups, the HRV parameters SDNN, HRVIndex, LF, and TP were significantly reduced in the group with palpitations (p < 0.05). Multivariate analysis revealed that HRVIndex (p = 0.016; OR: 0.966, 95% CI: 0.940∼0.994) had a significant impact on palpitation symptoms in female patients with long COVID.

Conclusions

The symptoms of palpitations in female patients with long COVID were found to be related to HRV parameters. Autonomic dysfunction may be connected to the occurrence of palpitation symptoms in long COVID.

ComsystanJ: A collection of Fiji/ImageJ2 plugins for nonlinear and complexity analysis in 1D, 2D and 3D

Complex systems such as the global climate, biological organisms, civilisation, technical or social networks exhibit diverse behaviours at various temporal and spatial scales, often characterized by nonlinearity, feedback loops, and emergence. These systems can be characterized by physical quantities such as entropy, information, chaoticity or fractality rather than classical quantities such as time, velocity, energy or temperature. The drawback of these complexity quantities is that their definitions are not always mathematically exact and computational algorithms provide estimates rather than exact values. Typically, evaluations can be cumbersome, necessitating specialized tools. We are therefore introducing ComsystanJ, a novel and user-friendly software suite, providing a comprehensive set of plugins for complex systems analysis, without the need for prior programming knowledge. It is platform independent, end-user friendly and extensible. ComsystanJ combines already known algorithms and newer methods for generalizable analysis of 1D signals, 2D images and 3D volume data including the generation of data sets such as signals and images for testing purposes. It is based on the framework of the open-source image processing software Fiji and ImageJ2. ComsystanJ plugins are macro recordable and are maintained as open-source software. ComsystanJ includes effective surrogate analysis in all dimensions to validate the features calculated by the different algorithms. Future enhancements of the project will include the implementation of parallel computing for image stacks and volumes and the integration of artificial intelligence methods to improve feature recognition and parameter calculation.

Autonomic nervous system flexibility for understanding brain aging

A recent call was made for autonomic nervous system (ANS) measures as digital health markers for early detection of Alzheimer's disease and related dementia (AD/ADRD). Nevertheless, contradictory or inconclusive findings exist. To help advance understanding of ANS' role in dementia, we draw upon aging and dementia-related literature, and propose a framework that centers on the role of ANS flexibility to guide future work on application of ANS function to differentiating the degree and type of dementia-related brain pathologies. We first provide a brief review of literature within the past 10 years on ANS and dementia-related brain pathologies. Next, we present an ANS flexibility model, describing how the model can be applied to understand these brain pathologies, as well as differentiate or even be leveraged to modify typical brain aging and dementia. Lastly, we briefly discuss the implication of the model for understanding resilience and vulnerability to dementia-related outcomes.

A Serious Game to Self-Regulate Heart Rate Variability as a Technique to Manage Arousal Level Through Cardiorespiratory Biofeedback: Development and Pilot Evaluation Study

BACKGROUND

Heart rate variability biofeedback (HRVB) is an established intervention for increasing heart rate variability (HRV) in the clinical context. Using this technique, participants become aware of their HRV through real-time feedback and can self-regulate it.

OBJECTIVE

The aim of this study was 2-fold: first, to develop a serious game that applies the HRVB technique to teach participants to self-regulate HRV and, second, to test the app with participants in a pilot study.

METHODS

An HRVB app called the FitLab Game was developed for this study. To play the game, users must move the main character up and down the screen, avoiding collisions with obstacles. The wavelength that users must follow to avoid these obstacles is based on the user's basal heart rate and changes in instantaneous heart rate. To test the FitLab Game, a total of 16 participants (mean age 23, SD 0.69 years) were divided into a control group (n=8) and an experimental group (n=8). A 2 × 2 factorial design was used in each session. Participants in the experimental condition were trained in breathing techniques.

RESULTS

Changes in the frequency and time domain parameters of HRV and the game's performance features were evaluated. Significant changes in the average RR intervals and root mean square of differences between adjacent RR intervals (RMSSD) were found between the groups (P=.02 and P=.04, respectively). Regarding performance, both groups showed a tendency to increase the evaluated outcomes from baseline to the test condition.

CONCLUSIONS

The results may indicate that playing different levels leads to an improvement in the game's final score by repeated training. The tendency of changes in HRV may reflect a higher activation of the mental system of attention and control in the experimental group versus the control group. In this context, learning simple, voluntary strategies through a serious game can aid the improvement of self-control and arousal management. The FitLab Game appears to be a promising serious game owing to its ease of use, high engagement, and enjoyability provided by the instantaneous feedback.

The effect of paired associative stimulation with a high-intensity cortical component and a high-frequency peripheral component on heart rate and heart rate variability in healthy subjects

Objective

A novel protocol for paired associative stimulation (PAS), called high PAS, consists of high-intensity transcranial magnetic stimulation (TMS) and high-frequency peripheral nerve stimulation (PNS). High PAS was developed for spinal cord injury rehabilitation and targets plastic changes in stimulated pathways in the corticospinal tract, which improves motor function. As therapy interventions can last many weeks, it is important to fully understand the effects of high PAS, including its effect on the cardiovascular system. Heart rate variability (HRV) has been used to measure changes in both sympathetic and parasympathetic systems.

Methods

We used short-term HRV measurements to evaluate the effects of one 20-min session of high PAS on 17 healthy individuals. HRV was recorded for 5 min before (PRE), during (STIM), immediately after (POST), 30 min after (POST30), and 60 min after (POST60) the stimulation. Five participants repeated the HRV setup with sham stimulation.

Results

A significant decrease in low-frequency (LF) power (n.u.) (p = 0.002), low-frequency to high-frequency (HF) ratio (p = 0.017), in Poincaré plot [the standard deviation of RR intervals perpendicular to (SD1) and along (SD2) the line of identity SD2/SD1 ratio p < 0.001], and an increase in HF power (n.u.) (p = 0.002) were observed between PRE and STIM conditions; these changes were fully reversible immediately after stimulation. PRE to POST by 3% (p = 0.015) and continued to decline until POST60 by 5% (p = 0.011). LF power (ms2) (p = 0.017) and SD2 (p = 0.015) decreased from PRE to STIM and increased from PRE to POST (p = 0.025 and p = 0.017, respectively). The results from sham PAS exhibited a trend similar to active high-PAS stimulation.

Conclusions

High PAS does not have sustained effects during 60-min follow-up on cardiovascular functions, as measured by HRV. None of the short-term results indicates activation of the sympathetic nervous system in healthy individuals. Observed changes in HRV indicate higher parasympathetic activity during stimulation, which is reversible, and is plausibly explained by the fact that the participants spend 20 min without moving, talking, or using phones while being stimulated.

Heart rate variability changes with respect to time and exercise intensity during heart-rate-controlled steady-state treadmill running

The aim of this work was to investigate the time and exercise intensity dependence of heart rate variability (HRV). Time-dependent, cardiovascular-drift-related increases in heart rate (HR) were inhibited by enforcing a constant heart rate throughout the exercise with a feedback control system. Thirty-two healthy adults performed HR-stabilised treadmill running exercise at two distinct exercise intensity levels. Standard time and frequency domain HRV metrics were computed and served as outcomes. Significant decreases were detected in 8 of the 14 outcomes for the time dependence analysis and in 6 of the 7 outcomes for the exercise intensity dependence analysis (excluding the experimental speed-signal frequency analysis). Furthermore, metrics that have been reported to reach an intensity-dependent near-zero minimum rapidly (usually at moderate intensity) were found to be near constant over time and only barely decreased with intensity. Taken together, these results highlight that HRV generally decreases with time and with exercise intensity. The intensity-related reductions were found to be greater in value and significance compared to the time-related reductions. Additionally, the results indicate that decreases in HRV metrics with time or exercise intensity are only detectable as long as their metric-specific near-zero minimum has not yet been reached.

Screening for Major Depressive Disorder Using a Wearable Ultra-Short-Term HRV Monitor and Signal Quality Indices

To encourage potential major depressive disorder (MDD) patients to attend diagnostic sessions, we developed a novel MDD screening system based on sleep-induced autonomic nervous responses. The proposed method only requires a wristwatch device to be worn for 24 h. We evaluated heart rate variability (HRV) via wrist photoplethysmography (PPG). However, previous studies have indicated that HRV measurements obtained using wearable devices are susceptible to motion artifacts. We propose a novel method to improve screening accuracy by removing unreliable HRV data (identified on the basis of signal quality indices (SQIs) obtained by PPG sensors). The proposed algorithm enables real-time calculation of signal quality indices in the frequency domain (SQI-FD). A clinical study conducted at Maynds Tower Mental Clinic enrolled 40 MDD patients (mean age, 37.5 ± 8.8 years) diagnosed on the basis of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and 29 healthy volunteers (mean age, 31.9 ± 13.0 years). Acceleration data were used to identify sleep states, and a linear classification model was trained and tested using HRV and pulse rate data. Ten-fold cross-validation showed a sensitivity of 87.3% (80.3% without SQI-FD data) and specificity of 84.0% (73.3% without SQI-FD data). Thus, SQI-FD drastically improved sensitivity and specificity.

Humming (Simple Bhramari Pranayama) as a Stress Buster: A Holter-Based Study to Analyze Heart Rate Variability (HRV) Parameters During Bhramari, Physical Activity, Emotional Stress, and Sleep

Objective In this study, our goal was to understand the comparative impact of humming, physical activity, emotional stress, and sleep on several heart rate variability (HRV) parameters, including the stress index (SI), and to assess the effectiveness of humming (simple Bhramari) as a stress buster based on the HRV parameters. Methods This pilot study assessed the long-term HRV parameters of 23 participants in terms of four activities: humming (simple Bhramari), physical activity, emotional stress, and sleep. The single-channel Holter device measured the readings, and data was analyzed using Kubios HRV Premium software for time and frequency-domain HRV parameters, including the stress index. Regarding statistical analysis, single-factor ANOVA followed by paired t-test was used to compare the results of HRV parameters "during" the four activities to understand if humming generates the outcome to enhance the autonomic nervous system. Results Our findings revealed that humming generates the lowest stress index compared to all three other activities (physical activity, emotional stress, and sleep). Several additional HRV parameters also supported the positive impact on the autonomic nervous, equivalent to stress reduction. Conclusions Humming (simple Bhramari) can be an effective stress-buster based on the assessment of several HRV parameters during its practice and in comparison with other activities. A regular daily humming routine can help enhance the parasympathetic nervous system and slow down sympathetic activation.

Can heart rate variability be a bio-index of hope? A pilot study

Background

Hope can affect the thinking habits, emotional regulations, and behaviors of individuals. Hope is considered as a positive trait by clinicians, who often assess the level of hope in psychological evaluations. Previous measurements of hope were largely based on self-reported questionnaires leading to the problem of subjectivity. Heart Rate Variability (HRV) is a bio index that is an objective, quick, cost effective, and non-invasive measurement. HRV has been used in the evaluation of physical health and some psychiatric conditions. However, it has not been tested for its potential to be a bio-index of the level of hope.

Method

This pilot cross-sectional observational study aimed to examine the relationships between HRV and the level of hope among adult Chinese people in Hong Kong. Convenience sampling was used and 97 healthy participants were recruited. Their level of hope was measured by the Dispositional Hope Scale-Chinese (DHS-C), and their HRV was quantified by emWave Pro Plus, a reliable sensor of HRV. Spearman's correlation coefficient analysis was performed on the HRV measurements and DHS-C.

Results

The DHS-C's overall mean score was 45.49. The mean scores of the subscale DHS-C (Agency) was 22.46, and the mean scores of DHS-C (Pathway) was 23.03. It was also revealed that there were significant, weak, and negative correlations between the level of hope and four out of ten HRV metrics. One HRV metric was found to have a significant, weak, and positive correlation with the level of hope.

Conclusion

This study provided initial evidence to support the use of HRV as a bio-index of hope. Implications of the current study and recommendations for future research directions are discussed.

A fast sample entropy for pulse rate variability analysis

Sample entropy is an effective nonlinear index for analyzing pulse rate variability (PRV) signal, but it has problems with a large amount of calculation and time consumption. Therefore, this study proposes a fast sample entropy calculation method to analyze the PRV signal according to the microprocessor process of data updating and the principle of sample entropy. The simulated data and PRV signal are employed as experimental data to verify the accuracy and time consumption of the proposed method. The experimental results on simulated data display that the proposed improved sample entropy can improve the operation rate of the entropy value by a maximum of 47.6 times and an average of 28.6 times and keep the entropy value unchanged. Experimental results on PRV signal display that the proposed improved sample entropy has great potential in the real-time processing of physiological signals, which can increase approximately 35 times.

The potential of biophysiology for understanding motivation, engagement and learning experiences

BACKGROUND AND AIMS

Integrative models applied to human learning and performance emphasize the joint operation of biological, psychological, social, and educational processes to fully understand human functioning. Educational psychology researchers have typically emphasized psycho-educational and psycho-social factors in motivation, engagement and learning, but do not often consider the biophysiological factors.

RESULTS

This Editorial and Special Issue advances current understanding on the role of biophysiological factors and processes in students' and teachers' motivation, engagement, and learning experiences, by showcasing recent educational research that included biophysiological measures and methods.

CONCLUSIONS

As we discuss, conducting integrative biophysiological and psycho-educational research has potential to derive vital substantive, methodological, and applied insights that provide a rigorous basis for more effective educational theory, research, and practice.

Influences of Intense Physical Effort on the Activity of the Autonomous Nervous System and Stress, as Measured with Photoplethysmography

Background: The autonomic nervous system, which is composed of the sympathetic and parasympathetic nervous system, is closely related to the cardiovascular system. The temporal variation between each of the intervals between the consecutive “R” waves of an electrocardiogram is known as heart rate variability. Depending on the type of activity, both systems can be activated, and also influence the interval between “R” waves. Currently, with advancements in technology and electronic devices, photoplethysmography is used. Photoplethysmography detects changes in the intensity of reflected light that allow differentiation between systole and diastole and, therefore, determines the heart rate, its frequency and its variations. In this way, changes in the autonomic nervous system can be detected by devices such as the Max Pulse®. Objective: To determine whether the information provided by Max Pulse® on autonomic balance and stress is modified after intense physical exercise, thereby determining whether there is a relationship with body composition, and also whether there are differences with respect to gender. Materials and Methods: Fifty-three runners (38.9% female) with a mean age of 31.3 ± 8.1 years participated in the study. Two measurements (before and after intense physical effort) were performed with the Max Pulse® device. The flotoplethysmography measurement lasted 3 min, and was performed in the supine position. The exercise test was performed on a treadmill. It was initiated at a speed of 6 and 7 km/h for women and men, respectively. Subjects indicated the end of the test by making a hand gesture when unable to continue the test. Results: Autonomic nervous system activity and mental stress values decreased significantly (p < 0.05) in men and women, while autonomic nervous system balance decreased only in women. Physical stress increased (p < 0.05) in both sexes. Conclusions: Intense exercise causes changes in variables that assess autonomic nervous system balance and stress, as measured by a device based on photoplethysmography. The changes are evident in both sexes, and are not related to body composition.

A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis

Artifacts in the Electrocardiogram (ECG) degrade the quality of the recorded signal and are not conducive to heart rate variability (HRV) analysis. The two types of noise most often found in ECG recordings are technical and physiological artifacts. Current preprocessing methods primarily attend to ectopic beats but do not consider technical issues that affect the ECG. A secondary aim of this study was to investigate the effect of increasing increments of artifacts on 24 of the most used HRV measures. A two-step preprocessing approach for denoising HRV is introduced which targets each type of noise separately. First, the technical artifacts in the ECG are eliminated by applying complete ensemble empirical mode decomposition with adaptive noise. The second step removes physiological artifacts from the HRV signal using a combination filter of single dependent rank order mean and an adaptive filtering algorithm. The performance of the two-step pre-processing tool showed a high correlation coefficient of 0.846 and RMSE value of 7.69 × 10^-5 for 6% of added ectopic beats and 6 dB Gaussian noise. All HRV measures studied except HF peak and LF peak are significantly affected by both types of noise. Frequency measures of Total power, HF power, and LF power and fragmentation measures; PAS, PIP, and PSS are the most sensitive to both types of noise.

A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree

Extreme bradycardia (EB), extreme tachycardia (ET), ventricular tachycardia (VT), and ventricular flutter (VF) are the four types of life-threatening arrhythmias, which are symptoms of cardiovascular diseases. Therefore, in this study, a method of life-threatening arrhythmia recognition is proposed based on pulse rate variability (PRV). First, noise and interference are wiped out from the arterial blood pressure (ABP), and the PRV signal is extracted. Then, 19 features are extracted from the PRV signal, and 15 features with highly important and significant variation were selected by random forest (RF). Finally, the back-propagation neural network (BPNN), extreme learning machine (ELM), and decision tree (DT) are used to build, train, and test classifiers to detect life-threatening arrhythmias. The experimental data are obtained from the MIMIC/Fantasia and the 2015 Physiology Net/CinC Challenge databases. The experimental results show that the DT classifier has the best average performance with accuracy and kappa coefficient (kappa) of 98.76 ± 0.08% and 97.59 ± 0.15%, which are higher than those of the BPNN (accuracy = 94.85 ± 1.33% and kappa = 89.95 ± 2.62%) and ELM (accuracy = 95.05 ± 0.14% and kappa = 90.28 ± 0.28%) classifiers. The proposed method shows better performance in identifying four life-threatening arrhythmias compared to existing methods and has potential to be used for home monitoring of patients with life-threatening arrhythmias.

Machine Learning Methods in Predicting Patients with Suspected Myocardial Infarction Based on Short-Time HRV Data

Diagnosis of cardiovascular diseases is an urgent task because they are the main cause of death for 32% of the world's population. Particularly relevant are automated diagnostics using machine learning methods in the digitalization of healthcare and introduction of personalized medicine in healthcare institutions, including at the individual level when designing smart houses. Therefore, this study aims to analyze short 10-s electrocardiogram measurements taken from 12 leads. In addition, the task is to classify patients with suspected myocardial infarction using machine learning methods. We have developed four models based on the k-nearest neighbor classifier, radial basis function, decision tree, and random forest to do this. An analysis of time parameters showed that the most significant parameters for diagnosing myocardial infraction are SDNN, BPM, and IBI. An experimental investigation was conducted on the data of the open PTB-XL dataset for patients with suspected myocardial infarction. The results showed that, according to the parameters of the short ECG, it is possible to classify patients with a suspected myocardial infraction as sick and healthy with high accuracy. The optimized Random Forest model showed the best performance with an accuracy of 99.63%, and a root mean absolute error is less than 0.004. The proposed novel approach can be used for patients who do not have other indicators of heart attacks.

Wearables in Cardiovascular Disease

Wearable devices stand to revolutionize the way healthcare is delivered. From consumer devices that provide general health information and screen for medical conditions to medical-grade devices that allow collection of larger datasets that include multiple modalities, wearables have a myriad of potential uses, especially in cardiovascular disorders. In this review, we summarize the underlying technologies employed in these devices and discuss the regulatory and economic aspects of such devices as well as the future implications of their use.

An Observational Study of Heart Rate Variability Using Wearable Sensors Provides a Target for Therapeutic Monitoring of Autonomic Dysregulation in Patients with Rett Syndrome

Rett Syndrome (RTT) is a complex neurodevelopmental disorder that has multi-system involvement with co-occurring epilepsy, breathing problems and autonomic dysregulation. Autonomic dysregulation can increase the risk of cardiorespiratory vulnerability in this patient group. Assessment of heart rate variability (HRV) provides an overview of autonomic health in RTT and offers insight into how the sympathetic and parasympathetic components of the nervous system function. However, to our knowledge, no study has evaluated HRV in Rett patients to assess how the dynamics of autonomic function vary with age and changes during the day and/or night. Using non-invasive wearable sensors, we measured HRV in 45 patients with RTT and examined the time and frequency domain sympathetic and parasympathetic indices. Among the HRV indices assessed, heart rate decreases with age and is lower in the night across all ages studied. The sympathetic index (SDNN) and the parasympathetic indices (RMSSD and pNN50) are not seen to change with age. Nevertheless, these indices were all higher during the day when compared to the night. Our findings appear to show that Rett patients are less adaptable to autonomic changes during the night. In the clinical setting, this might be more relevant for patients with severe psychopathology.

Chronotropic Response and Heart Rate Variability before and after a 160 m Walking Test in Young, Middle-Aged, Frail, and Non-Frail Older Adults

The frailty syndrome is characterized by a decreased capacity to adequately respond to stressors. One of the most impaired physiological systems is the autonomous nervous system, which can be assessed through heart rate (HR) variability (HRV) analysis. In this article, we studied the chronotropic response (HR and HRV) to a walking test. We also analyzed HRV indices in rest as potential biomarkers of frailty. For this, a 160 m-walking test and two standing rest tests (before and after the walking) were performed by young (19−29 years old, n = 21, 57% women), middle-aged (30−59 years old, n = 16, 62% women), and frail older adults (>60 years old, n = 28, 40% women) and non-frail older adults (>60 years old, n = 15, 71% women), classified with the FRAIL scale and the Clinical Frailty Scale (CFS). Frequency domain parameters better allowed to distinguish between frail and non-frail older adults (low-frequency power LF, high-frequency power HF (nu), LF/HF ratio, and ECG-derived respiration rate EDR). Frail older adults showed an increased HF (nu) and EDR and a reduced LF (nu) and LF/HF compared to non-frail older adults. The increase in HF (nu) could be due to a higher breathing effort. Our results showed that a walk of 160 m is a sufficient cardiovascular stressor to exhibit an attenuated autonomic response in frail older adults. Several HRV indices showed to be potential biomarkers of frailty, being LF (nu) and the time required to reach the maximum HR the best candidates.

Affective State Recognition with Convolutional Autoencoders

The aim of this study was to create a robust generalizable model to classify person's affective state based on physiological signals obtained using wearable sensor devices. Traditional machine learning methods require manual feature extraction from time sequences. Deep learning methods, such as Convolutional Neural Networks (CNN), can automatically extract features from time sequences. However, CNN models can be prone to overfitting, especially when the dataset is small. We apply a novel idea of using unsupervised convolutional autoencoders to automatically extract features from time-series data that are then fed to supervised classifier to classify people's affective state. We achieve almost 3% accuracy increase over traditional CNN model using all physio data from WESAD dataset, 2% increase using chest only physio data, and 8% increase using wrist only physio data while classifying neutral, stress, and amusement states. Code to reproduce the results can be found at https://github.com/srovins/wesad Clinical Relevance- A high-performing affective state recognition system can be utilized for various medical applications, ranging from patient monitoring to cognitive therapy.

Wearable Flexible Electronics Based Cardiac Electrode for Researcher Mental Stress Detection System Using Machine Learning Models on Single Lead Electrocardiogram Signal

In the modern world, wearable smart devices are continuously used to monitor people's health. This study aims to develop an automatic mental stress detection system for researchers based on Electrocardiogram (ECG) signals from smart T-shirts using machine learning classifiers. We used 20 subjects, including 10 from mental stress (after twelve hours of continuous work in the laboratory) and 10 from normal (after completing the sleep or without any work). We also applied three scoring techniques: Chalder Fatigue Scale (CFS), Specific Fatigue Scale (SFS), Depression, Anxiety, and Stress Scale (DASS), to confirm the mental stress. The total duration of ECG recording was 1800 min, including 1200 min during mental stress and 600 min during normal. We calculated two types of features, such as demographic and extracted by ECG signal. In addition, we used Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Logistic Regression (LR) to classify the intra-subject (mental stress and normal) and inter-subject classification. The DT leave-one-out model has better performance in terms of recall (93.30%), specificity (96.70%), precision (94.40%), accuracy (93.30%), and F1 (93.50%) in the intra-subject classification. Additionally, The classification accuracy of the system in classifying inter-subjects is 94.10% when using a DT classifier. However, our findings suggest that the wearable smart T-shirt based on the DT classifier may be used in big data applications and health monitoring. Mental stress can lead to mitochondrial dysfunction, oxidative stress, blood pressure, cardiovascular disease, and various health problems. Therefore, real-time ECG signals help assess cardiovascular and related risk factors in the initial stage based on machine learning techniques.

Convolutional Neural Networks for Mechanistic Driver Detection in Atrial Fibrillation

The maintaining and initiating mechanisms of atrial fibrillation (AF) remain controversial. Deep learning is emerging as a powerful tool to better understand AF and improve its treatment, which remains suboptimal. This paper aims to provide a solution to automatically identify rotational activity drivers in endocardial electrograms (EGMs) with convolutional recurrent neural networks (CRNNs). The CRNN model was compared with two other state-of-the-art methods (SimpleCNN and attention-based time-incremental convolutional neural network (ATI-CNN)) for different input signals (unipolar EGMs, bipolar EGMs, and unipolar local activation times), sampling frequencies, and signal lengths. The proposed CRNN obtained a detection score based on the Matthews correlation coefficient of 0.680, an ATI-CNN score of 0.401, and a SimpleCNN score of 0.118, with bipolar EGMs as input signals exhibiting better overall performance. In terms of signal length and sampling frequency, no significant differences were found. The proposed architecture opens the way for new ablation strategies and driver detection methods to better understand the AF problem and its treatment.

Heart rate variability in obsessive compulsive disorder in comparison to healthy controls and as predictor of treatment response

OBJECTIVE

Obsessive compulsive disorder (OCD) has a large impact on the quality of life of patients. It often takes years to get the right diagnosis and to receive treatment. Therefore, biomarkers that could inform the diagnostic process and provide information on response or non-response for first line treatment approaches are urgently needed. The aim of this study was to analyze whether (1) heart rate (HR) and heart rate variability (HRV) markers of the autonomous nervous system could distinguish between healthy controls (HC) and patients suffering from OCD and (2) HRV parameters additionally yield useful information to separate therapy-responders from non-responders.

METHODS

A fifteen-minute resting state ECG (electrocardiogram) was recorded from 51 unmedicated OCD patients before treatment and 28 HC. The function of the autonomic nervous system was assessed by using parameters of the HRV. Clinical Global Impression (CGI) scores served as baseline and outcome parameters following three to six months of therapy (cognitive behavioral therapy n = 18, selective-serotonin-reuptake-inhibitor n = 11 or combination n = 22). Differences between patients and HC and responders and non-responders were identified using analysis of covariance (ANCOVAs). Predictive values were calculated following binary regression modelling and receiver operating characteristics (ROC).

RESULTS

OCD patients revealed a significantly higher HR in comparison to HC. Although patients were thus characterized by increased sympathetic and decreased parasympathetic tone, treatment responders exhibited a larger High Frequency Power as a marker for increased parasympathetic activity at baseline. ROC-curves for OCD vs HC and R vs NR showed clinically relevant areas under curve (83%, 88% respectively).

CONCLUSIONS

These results are in line with findings of increased sympathetic and decreased parasympathetic activity in OCD in comparison to healthy subjects. The findings further provide clinically useful information on treatment response in OCD.

SIGNIFICANCE

Results may facilitate the clinical use of electrophysiological markers in OCD.

Synchronization of the Processes of Autonomic Control of Blood Circulation in Humans Is Different in the Awake State and in Sleep Stages

The influence of higher nervous activity on the processes of autonomic control of the cardiovascular system and baroreflex regulation is of considerable interest, both for understanding the fundamental laws of the functioning of the human body and for developing methods for diagnostics and treatment of pathologies. The complexity of the analyzed systems limits the possibilities of research in this area and requires the development of new tools. Earlier we propose a method for studying the collective dynamics of the processes of autonomic control of blood circulation in the awake state and in different stages of sleep. The method is based on estimating a quantitative measure representing the total percentage of phase synchronization between the low-frequency oscillations in heart rate and blood pressure. Analysis of electrocardiogram and invasive blood pressure signals in apnea patients in the awake state and in different sleep stages showed a high sensitivity of the proposed measure. It is shown that in slow-wave sleep the degree of synchronization of the studied rhythms is higher than in the awake state and lower than in sleep with rapid eye movement. The results reflect the modulation of the processes of autonomic control of blood circulation by higher nervous activity and can be used for the quantitative assessment of this modulation.

Quantitative measurements of zebrafish heartrate and heart rate variability: A survey between 1990-2020

Zebrafish is an essential model organism for studying cardiovascular diseases, given its advantages of fast proliferation and high gene homology with humans. Zebrafish embryos/larvae are valuable experimental models used in toxicology studies to analyze drug toxicity, including hepatoxicity, nephrotoxicity and cardiotoxicity, as well as for drug discovery and drug safety screening in the preclinical stage. Heart rate (HR) serves as a functional endpoint in studies of cardiotoxicity, while heart rate variability (HRV) serves as an indicator of cardiac arrhythmia. Cardiotoxicity is a major cause of early and late termination of drug trials, so a more comprehensive understanding of zebrafish HR and HRV is important. This review summarized HR and HRV in a specific range of applications and fields, focusing on zebrafish heartbeat detection procedures, signal analysis technology and well-established commercial software, such as LabVIEW, Rvlpulse, and ZebraLab. We also compared HR detection algorithms and electrocardiography (ECG)-based methods of heart signal extraction. The relationship between HR and HRV was also systematically analyzed; HR was shown to have an inverse correlation with HRV. Applications to drug testing are also highlighted in this review. Furthermore, HR and HRV were shown to be regulated by the automatic nervous system; their connections with ECG measurements are also summarized herein.

Review of Deep Learning-Based Atrial Fibrillation Detection Studies

Atrial fibrillation (AF) is a common arrhythmia that can lead to stroke, heart failure, and premature death. Manual screening of AF on electrocardiography (ECG) is time-consuming and prone to errors. To overcome these limitations, computer-aided diagnosis systems are developed using artificial intelligence techniques for automated detection of AF. Various machine learning and deep learning (DL) techniques have been developed for the automated detection of AF. In this review, we focused on the automated AF detection models developed using DL techniques. Twenty-four relevant articles published in international journals were reviewed. DL models based on deep neural network, convolutional neural network (CNN), recurrent neural network, long short-term memory, and hybrid structures were discussed. Our analysis showed that the majority of the studies used CNN models, which yielded the highest detection performance using ECG and heart rate variability signals. Details of the ECG databases used in the studies, performance metrics of the various models deployed, associated advantages and limitations, as well as proposed future work were summarized and discussed. This review paper serves as a useful resource for the researchers interested in developing innovative computer-assisted ECG-based DL approaches for AF detection.

Validation of a Low-Cost Electrocardiography (ECG) System for Psychophysiological Research

BACKGROUND AND OBJECTIVE

The reliability of low-cost mobile systems for recording Electrocardiographic (ECG) data is mostly unknown, posing questions regarding the quality of the recorded data and the validity of the extracted physiological parameters. The present study compared the BITalino toolkit with an established medical-grade ECG system (BrainAmp-ExG).

METHODS

Participants underwent simultaneous ECG recordings with the two instruments while watching pleasant and unpleasant pictures of the "International Affective Picture System" (IAPS). Common ECG parameters were extracted and compared between the two systems. The Intraclass Correlation Coefficients (ICCs) and the Bland-Altman Limits of Agreement (LoA) method served as criteria for measurement agreement.

RESULTS

All but one parameter showed an excellent agreement (>80%) between both devices in the ICC analysis. No criteria for Bland-Altman LoA and bias were found in the literature regarding ECG parameters.

CONCLUSION

The results of the ICC and Bland-Altman methods demonstrate that the BITalino system can be considered as an equivalent recording device for stationary ECG recordings in psychophysiological experiments.