Fundamental Considerations of HRV Analysis in the Development of Real-Time Biofeedback Systems

Heart rate variability (HRV) biofeedback training is known for its effectiveness in improving physical health, emotional health, and resilience by the ability to regulate heart rhythm. However, there are various challenges in delivering and interpreting the biofeedback information, which prevents an optimal experience. Therefore, this study presents the fundamentals of developing a real-time HRV biofeedback system using deep breathing exercise by exploring the minimum time window of RR-intervals resulting in a reliable analysis. Moreover, it investigates the appropriate HRV measures by examining the significant changes between resting and breathing conditions and the trends consistency across ultra-short-term segments. The overall results suggest that a minimum time window of 20-seconds can provide a reliable HRV time-domain analysis. Whereas the possible HRV measures that can be used in a real-time biofeedback system are SDNN, LF, and total power. These outcomes will contribute to the design of a self-monitoring HRV biofeedback system based on a multi-modal approach

Evaluation of two detrending techniques for application in Heart Rate Variability

The performance of two different algorithms of detrending the RR-interval before Heart Rate Variability (HRV) analysis has been evaluated using both, simulated signals and real RR-interval time series. The first algorithm is based on the smoothness prior approach (SPA) and the second algorithm is implemented using wavelet packet (WP) analysis. The calculated time and frequency domain parameters obtained from real signals after detrending and the results obtained from simulated signals suggest that the WP method performed better than the SPA. The WP method provided more attenuation of the slow varying trend and was able to preserve the other signal components better than the SPA method. Also the SPA method was computationally slower and it might be not appropriate with long signals

Feedback Control of Human Stress with Music Modulation

Mental stress has known detrimental effects on human health, however few algorithmic methods of reducing mental stress have been widely explored. While the act of listening to music has been shown to have beneficial effects for stress reduction, and furthermore, audio players have been designed to selectively choose music and other inputs with the intent of stress reduction, limited work has been conducted for real-time stress reduction with feedback control using physiological input signals such as heart rate or Heart Rate Variability (HRV). This thesis proposes a feedback controller that uses HRV signals from wearable sensors to perform real-time (< 1 second) modulations to music through tempo changes with the goal to regulate and reduce stress levels. A standardized, stress inducing test based on the popular Stroop test is also introduced, which has been shown to induce acute stress in subjects and can be used as a testing benchmark for controller design. Ultimately, a controller is presented that when used is not only able to maintain stress levels during stress-inducing inputs to a human but even provides de-stressing effects beyond baseline performance.No embargoAcademic Major: Electrical and Computer Engineerin

Estimation of confidence limits for descriptive indexes derived from autoregressive analysis of time series: Methods and application to heart rate variability

The growing interest in personalized medicine requires making inferences from descriptive indexes estimated from individual recordings of physiological signals, with statistical analyses focused on individual differences between/within subjects, rather than comparing supposedly homogeneous cohorts. To this end, methods to compute confidence limits of individual estimates of descriptive indexes are needed. This study introduces numerical methods to compute such confidence limits and perform statistical comparisons between indexes derived from autoregressive (AR) modeling of individual time series. Analytical approaches are generally not viable, because the indexes are usually nonlinear functions of the AR parameters. We exploit Monte Carlo (MC) and Bootstrap (BS) methods to reproduce the sampling distribution of the AR parameters and indexes computed from them. Here, these methods are implemented for spectral and information-theoretic indexes of heart-rate variability (HRV) estimated from AR models of heart-period time series. First, the MS and BC methods are tested in a wide range of synthetic HRV time series, showing good agreement with a gold-standard approach (i.e. multiple realizations of the "true" process driving the simulation). Then, real HRV time series measured from volunteers performing cognitive tasks are considered, documenting (i) the strong variability of confidence limits\ue2\u80\u99 width across recordings, (ii) the diversity of individual responses to the same task, and (iii) frequent disagreement between the cohort-average response and that of many individuals. We conclude that MC and BS methods are robust in estimating confidence limits of these AR-based indexes and thus recommended for short-term HRV analysis. Moreover, the strong inter-individual differences in the response to tasks shown by AR-based indexes evidence the need of individual-by-individual assessments of HRV features. Given their generality, MC and BS methods are promising for applications in biomedical signal processing and beyond, providing a powerful new tool for assessing the confidence limits of indexes estimated from individual recordings

Smart Device for the Determination of Heart Rate Variability in Real Time

This work presents a first approach to the design, development, and implementation of a smart device for the real-time measurement and detection of alterations in heart rate variability (HRV). The smart device follows a modular design scheme, which consists of an electrocardiogram (ECG) signal acquisition module, a processing module and a wireless communications module. From five-minute ECG signals, the processing module algorithms perform a spectral estimation of the HRV. The experimental results demonstrate the viability of the smart device and the proposed processing algorithms.Fundación Pública Andaluza Progreso y Salud. Gobierno de Andalucía PI-0010-2013 y PI-0041-2014Ministerio de Economía y Competitividad (Instituto de Salud Carlos III) PI15 / 00306 y DTS15 / 00195CIBER-BBN INT-2-CAR

Nonlinear heart rate variability features for real-life stress detection. Case study : students under stress due to university examination

Background: This study investigates the variations of Heart Rate Variability (HRV) due to a real-life stressor and proposes a classifier based on nonlinear features of HRV for automatic stress detection. Methods: 42 students volunteered to participate to the study about HRV and stress. For each student, two recordings were performed: one during an on-going university examination, assumed as a real-life stressor, and one after holidays. Nonlinear analysis of HRV was performed by using Poincaré Plot, Approximate Entropy, Correlation dimension, Detrended Fluctuation Analysis, Recurrence Plot. For statistical comparison, we adopted the Wilcoxon Signed Rank test and for development of a classifier we adopted the Linear Discriminant Analysis (LDA). Results: Almost all HRV features measuring heart rate complexity were significantly decreased in the stress session. LDA generated a simple classifier based on the two Poincaré Plot parameters and Approximate Entropy, which enables stress detection with a total classification accuracy, a sensitivity and a specificity rate of 90%, 86%, and 95% respectively. Conclusions: The results of the current study suggest that nonlinear HRV analysis using short term ECG recording could be effective in automatically detecting real-life stress condition, such as a university examination