Impact of Using Different Levels of Threshold-Based Artefact Correction on the Quantification of Heart Rate Variability in Three Independent Human Cohorts

The following are available online at https://www.mdpi.com/2077-0383/9/2/325/s1, Figure S1: Example of a visual inspection of a R-R signal to find possible artefacts or premature contractions across Kubios filters; Figures S2 and S3: Differences on the Heart Rate Variability (HRV) time- and frequency-domains parameters respectively without considering the Very Strong filter; Figure S4: Differences between cohorts on the SDNN using different Kubios filters; Figure S5: Differences between cohorts on the pNN50 using different Kubios filters; and Figure S6: Differences between cohorts on the HF using different Kubios filters.Juan M.A. Alcantara, Abel Plaza-Florido, Jairo H. Migueles, Guillermo Sanchez-Delgado and Francisco J. Amaro-Gahete are supported by the Spanish Ministry of Education, Culture and Sport (FPU15/04059, FPU16/02760, FPU15/02645, FPU13/04365 and FPU14/04172 respectively). Guillermo Sanchez-Delgado is supported by the University of Granada Plan Propio de Investigación 2018 (Programa Contratos-Puente and Programa Perfeccionamiento de Docotres). Francisco J. Amaro-Gahete is supported by the University of Granada Plan Propio de Investigación 2019 (Programa Contratos-Puente). Guillermo Sanchez-Delgado and Borja Martinez-Tellez are supported by individual postdoctoral grants from the Fundación Alfonso Martin Escudero.Heart rate variability (HRV) is a non-invasive indicator of autonomic nervous system function. HRV recordings show artefacts due to technical and/or biological issues. The Kubios software is one of the most used software to process HRV recordings, offering different levels of threshold-based artefact correction (i.e., Kubios filters). The aim of the study was to analyze the impact of different Kubios filters on the quantification of HRV derived parameters from short-term recordings in three independent human cohorts. A total of 312 participants were included: 107 children with overweight/obesity (10.0 ± 1.1 years, 58% men), 132 young adults (22.2 ± 2.2 years, 33% men) and 73 middle-aged adults (53.6 ± 5.2 years, 48% men). HRV was assessed using a heart rate monitor during 10–15 min, and the Kubios software was used for HRV data processing using all the Kubios filters available (i.e., 6). Repeated-measures analysis of variance indicated significant differences in HRV derived parameters in the time-domain (all p < 0.001) across the Kubios filters in all cohorts, moreover similar results were observed in the frequency-domain. When comparing two extreme Kubios filters, these statistical differences could be clinically relevant, e.g. more than 10 ms in the standard deviation of all normal R-R intervals (SDNN). In conclusion, the results of the present study suggest that the application of different Kubios filters had a significant impact on HRV derived parameters obtained from short-term recordings in both time and frequency-domains.The study was funded by the Spanish Ministry of Economy and Competitiveness (DEP2013-47540 and DEP2016-79512-R), the Fondo de Investigación Sanitaria del Instituto de Salud Carlos III (PI13/01393), European Union Development Funds, the Fundación Iberoamericana de Nutrición (FINUT), the Redes Temáticas de Investigación Cooperativa RETIC (Red SAMID RD16/0022), the University of Granada Plan Propio de Investigación 2016 (Excellence actions: Unit of Excellence on Exercise and Health [UCEES]), and the Junta de Andalucía, Consejería de Conocimiento, Investigación y Universidades (FEDER: ref. SOMM17/6107/UGR)

Artificial intelligence for heart rate variability analyzing with arrhythmias

Introduction. Existing standards of Heart Rate Variability (HRV) technology limit its use to sinus rhythm. A small number of extrasystoles is allowed, if the device used has special procedures for the detection and replacement of ectopic complexes. However, it is important to expand the indicated limits of the applicability of the HRV technology. This specially regards the cases when the HRV technology looks promising in the diagnostics, as, for example, in atrial fibrillation and atrial flutter. Materials and Methods. All ECG measurements were performed on XAI-MEDICA® equipment and software. Processing of the obtained RR Series was carried out using the software Kubios® HRV Standard. All recommended HRV characteristics for Time-Domain, Frequency-Domain and Nonlinear were calculated. The purpose of the work. The article presents an artificial intelligence (AI) procedure for detecting episodes of arrhythmias and reconstruction of core patient’s rhythm, and demonstrates the efficacy of its use for the HRV analysis in patients with varying degrees of arrhythmias. The results of the study. It was shown efficiency of developed artificial intelligence procedure for HRV analyzing of patients with different level of arrhythmias. These were demonstrated for Time-Domain, Frequency-Domain and Nonlinear methods. The direct inclusion into review of Arrhythmia Episodes and the use of the initial RR Series leads to a significant distortion of the results of the HRV analysis for the whole set of methods and for all considered options for arrhythmia. Conclusion. High efficacy of operation of the procedure AI core rhythm extraction from initial RR Series for patients with arrhythmia was reported in all cases

Validity of the Polar V800 heart rate monitor to measure RR intervals at rest

Purpose To assess the validity of RR intervals and short-term heart rate variability (HRV) data obtained from the Polar V800 heart rate monitor, in comparison to an electrocardiograph (ECG). Method Twenty participants completed an active orthostatic test using the V800 and ECG. An improved method for the identification and correction of RR intervals was employed prior to HRV analysis. Agreement of the data was assessed using intra-class correlation coefficients (ICC), Bland–Altman limits of agreement (LoA), and effect size (ES). Results A small number of errors were detected between ECG and Polar RR signal, with a combined error rate of 0.086 %. The RR intervals from ECG to V800 were significantly different, but with small ES for both supine corrected and standing corrected data (ES 0.999 for both supine and standing corrected intervals. When analysed with the same HRV software no significant differences were observed in any HRV parameters, for either supine or standing; the data displayed small bias and tight LoA, strong ICC (>0.99) and small ES (≤0.029). Conclusions The V800 improves over previous Polar models, with narrower LoA, stronger ICC and smaller ES for both the RR intervals and HRV parameters. The findings support the validity of the Polar V800 and its ability to produce RR interval recordings consistent with an ECG. In addition, HRV parameters derived from these recordings are also highly comparable

Can heart rate variability parameters derived by a heart rate monitor differentiate between atrial fibrillation and sinus rhythm?

Background: Heart rate variability (HRV) parameters, and especially RMSSD (root mean squared successive differences in RR interval), could distinguish atrial fibrillation (AF) from sinus rhythm(SR) in horses, as was demonstrated in a previous study. If heart rate monitors (HRM) automatically calculating RMSSD could also distinguish AF from SR, they would be useful for the monitoring of AF recurrence. The objective of the study was to assess whether RMSSD values obtained from a HRM can differentiate AF from SR in horses. Furthermore, the impact of artifact correction algorithms, integrated in the analyses software for HRV analyses was evaluated. Fourteen horses presented for AF treatment were simultaneously equipped with a HRM and an electrocardiogram (ECG). A two-minute recording at rest, walk and trot, before and after cardioversion, was obtained. RR intervals used were those determined automatically by the HRM and by the equine ECG analysis software, and those obtained after manual correction of QRS detection within the ECG software. RMSSD was calculated by the HRM software and by dedicated HRV software, using six different artifact filters. Statistical analysis was performed using the Wilcoxon signed-rank test and receiver operating curves. Results: The HRM, which applies a low level filter, produced high area under the curve (AUC) (>0.9) and cut off values with high sensitivity and specificity. Similar results were obtained for the ECG, when low level artifact filtering was applied. When no artifact correction was used during trotting, an important decrease in AUC (0.75) occurred. Conclusion: In horses treated for AF, HRMs with automatic RMSSD calculations distinguish between AF and SR. Such devices might be a useful aid to monitor for AF recurrence in horses

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

Heart rate variability during exercise: A comparison of artefact correction methods

There is a need for standard practice in the collection and processing of RR interval data recorded using heart rate monitors (HRM) in research. This paper assessed the validity of (a) RR intervals and heart rate variability (HRV) data obtained using a HRM during incremental exercise, and (b) artefact correction methods. Eighteen participants completed an active orthostatic test and incremental running V̇O2MAX test, while simultaneous recordings using the V800 and an electrocardiogram were made. Artefacts were corrected by deletion; degree zero, linear, cubic and spline interpolation; and using Kubios HRV software. Agreement was assessed using percentage bias and effect size (ES), intra-class correlation coefficients (ICC) and Bland–Altman limits of agreement (LoA). The number of artefacts increased relative to the intensity of the exercise, to a peak of 4.46% during 80-100% V̇O2MAX. Correction of RR intervals was necessary with unacceptably increased percentage bias, LoA and ES and reduced ICC in all but supine and standing recordings. All correction methods resulted in data with reduced percentage bias and ES for resting and 60% V̇O2MAX, even when correction methods were applied, large amounts of variation were present in RMSSD, LF:HF ratio, SD1 and SampEn. Linear interpolation produced corrected RR intervals with the lowest bias and ES. However, caution should be given to HRV parameters at high exercise intensities, as large amounts of variation were still present. Recommendations for minimising recording artefacts are discussed, along with guidelines for their identification, correction and reporting.N/

Determining Clinically Relevant Measures for Evaluating Recovery in Collegiate Athletes using Heart Rate Variability and RESTQ-Sport.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

A comparison of robust Kalman filtering methods for artifact correction in heart rate variability analysis

La variabilidad de la frecuencia cardiaca (HRV) ha recibido una atención considerable por mucho años, ya que esta proporciona un valor cuantitativo para examinar el ritmo sinusal modulado para el sistema nervioso autónomo (SNA). El SNA juega un papel importante en campos clínicos y fisiológicos. El análisis de la HRV se puede realizar calculando varias medidas tanto en el domino del tiempo como en la frecuencia. Sin embargo, el cálculo de estas medidas se puede ver afectado por la presencia de artefactos o latidos ectópicos en registros de electrocardiogramas (ECG). Esto es particularmente cierto para registros ECG desde un monitor Holter. El objetivo de este trabajo fue estudiar el rendimiento de varios filtros de Kalman robustos para la corrección de artefactos. Para nuestros experimentos, se usaron dos bases de datos reales: el primer conjunto de datos incluye 10 series de tiempo de intervalos RR a partir de un generador de series de tiempo de intervalos RR realista. La segunda base de datos contiene 10 conjuntos de series de intervalos RR de cinco pacientes sanos y cinco pacientes que sufren una insuficiencia cardiaca congestiva. Se calculó la desviación estándar de los intervalos RR a partir de las señales filtradas. Los resultados se compararon con un reconocido software de procesamiento, mostrando comportamientos y valores similares. Adicionalmente, los métodos propuestos ofrecen resultados satisfactorios en comparación con el filtro de Kalman estándar.Heart rate variability (HRV) has received considerable attention for many years, since it provides a quantitative marker for examining the sinus rhythm modulated by the autonomic nervous system (ANS). The ANS plays an important role in clinical and physiological fields. HRV analysis can be performed by computing several time and frequency domain measurements. However, the computation of such measurements can be affected by the presence of artifacts or ectopic beats in the electrocardiogram (ECG) recording. This is particularly true for ECG recordings from Holter monitors. The aim of this work was to study the performance of several robust Kalman filters for artifact correction in Inter-beat (RR) interval time series. For our experiments, two data sets were used: the first data set included 10 RR interval time series from a realistic RR interval time series generator. The second database contains 10 sets of RR interval series from five healthy patients and five patients suffering from congestive heart failure. The standard deviation of the RR interval was computed over the filtered signals. Results were compared with a state of the art processing software, showing similar values and behavior. In addition, the proposed methods offer satisfactory results in contrast to standard Kalman filtering

HRV in an Integrated Hardware/Software System Using Artificial Intelligence to Provide Assessment, Intervention and Performance Optimization

Heart rate variability (HRV) is increasingly recognized as a central variable of interest in health maintenance, disease prevention and performance optimization. It is also a sensitive biomarker of health status, disease presence and functional abilities, acquiring and processing high fidelity inter beat interval data, along with other psychophysiological parameters that can assist in clinical assessment and intervention, population health studies/digital epidemiology and positive performance optimization. We describe a system using high-throughput artificial intelligence based on the KUBIOS platform to combine time, frequency and nonlinear data domains acquired by wearable or implanted biosensors to guide in clinical assessment, decision support and intervention, population health monitoring and individual self-regulation and performance enhancement, including the use of HRV biofeedback. This approach follows the iP4 health model which emphasizes an integral, personalized, predictive, preventive and participatory approach to human health and well-being. It therefore includes psychological, biological, genomic, sociocultural, evolutionary and spiritual variables as mutually interactive elements in embodying complex systems adaptation