The ClassA Framework: HRV Based Assessment of SNS and PNS Dynamics Without LF-HF Controversies

The powers of the low frequency (LF) and high frequency (HF) components of heart rate variability (HRV) have become the de facto standard metrics in the assessment of the stress response, and the related activities of the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS). However, the widely adopted physiological interpretations of the LF and HF components in SNS /PNS balance are now questioned, which puts under serious scrutiny stress assessments which employ the LF and HF components. To avoid these controversies, we here introduce the novel Classification Angle (ClassA) framework, which yields a family of metrics which quantify cardiac dynamics in three-dimensions. This is achieved using a finite-difference plot of HRV, which displays successive rates of change of HRV, and is demonstrated to provide sufficient degrees of freedom to determine cardiac deceleration and/or acceleration. The robustness and accuracy of the novel ClassA framework is verified using HRV signals from ten males, recorded during standardized stress tests, consisting of rest, mental arithmetic, meditation, exercise and further meditation. Comparative statistical testing demonstrates that unlike the existing LF-HF metrics, the ClassA metrics are capable of distinguishing both the physical and mental stress epochs from the epochs of no stress, with statistical significance (Bonferroni corrected p-value ≤ 0.025); HF was able to distinguish physical stress from no stress, but was not able to identify mental stress. The ClassA results also indicated that at moderate levels of stress, the extent of parasympathetic withdrawal was greater than the extent of sympathetic activation. Finally, the analyses and the experimental results provide conclusive evidence that the proposed nonlinear approach to quantify cardiac activity from HRV resolves three critical obstacles to current HRV stress assessments: (i) it is not based on controversial assumptions of balance between the LF and HF powers; (ii) its temporal resolution when estimating parasympathetic dominance is as little as 10 s of HRV data, while only 60 s to estimate sympathetic dominance; (iii) unlike LF and HF analyses, the ClassA framework does not require the prohibitive assumption of signal stationarity. The ClassA framework is unique in offering HRV based stress analysis in three-dimensions

Influence of Duodenal-Jejunal Implantation on Glucose Dynamics: A Pilot Study Using Different Nonlinear Methods

[EN] Diabetes is a disease of great and rising prevalence, with the obesity epidemic being a significant contributing risk factor. Duodenal¿jejunal bypass liner (DJBL) is a reversible implant that mimics the effects of more aggressive surgical procedures, such as gastric bypass, to induce weight loss. We hypothesized that DJBL also influences the glucose dynamics in type II diabetes, based on the induced changes already demonstrated in other physiological characteristics and parameters. In order to assess the validity of this assumption, we conducted a quantitative analysis based on several nonlinear algorithms (Lempel¿Ziv Complexity, Sample Entropy, Permutation Entropy, and modified Permutation Entropy), well suited to the characterization of biomedical time series. We applied them to glucose records drawn from two extreme cases available of DJBL implantation: before and after 10 months. The results confirmed the hypothesis and an accuracy of 86.4% was achieved with modified Permutation Entropy. Other metrics also yielded significant classification accuracy results, all above 70%, provided a suitable parameter configuration was chosen. With the Leave¿One¿Out method, the results were very similar, between 72% and 82% classification accuracy. There was also a decrease in entropy of glycaemia records during the time interval studied. These findings provide a solid foundation to assess how glucose metabolism may be influenced by DJBL implantation and opens a new line of research in this field.The Czech clinical partners were supported by DRO IKEM 000023001 and RVO VFN 64165. The Czech technical partners were supported by Research Centre for Informatics grant numbers CZ.02.1.01/0.0/16 - 019/0000765 and SGS16/231/OHK3/3T/13-Support of interactive approaches to biomedical data acquisition and processing. No funding was received to support this research work by the Spanish and British partnersCuesta Frau, D.; Novák, D.; Burda, V.; Abasolo, D.; Adjei, T.; Varela, M.; Vargas, B.... (2019). Influence of Duodenal-Jejunal Implantation on Glucose Dynamics: A Pilot Study Using Different Nonlinear Methods. Complexity. 2019. https://doi.org/10.1155/2019/6070518S2019Kassirer, J. P., & Angell, M. (1998). Losing Weight — An Ill-Fated New Year’s Resolution. New England Journal of Medicine, 338(1), 52-54. doi:10.1056/nejm199801013380109Van Gaal, L., & Dirinck, E. (2016). Pharmacological Approaches in the Treatment and Maintenance of Weight Loss. Diabetes Care, 39(Supplement 2), S260-S267. doi:10.2337/dcs15-3016De Jonge, C., Rensen, S. S., Verdam, F. J., Vincent, R. P., Bloom, S. R., Buurman, W. A., … Greve, J. W. M. (2015). Impact of Duodenal-Jejunal Exclusion on Satiety Hormones. Obesity Surgery, 26(3), 672-678. doi:10.1007/s11695-015-1889-yMuñoz, R., Dominguez, A., Muñoz, F., Muñoz, C., Slako, M., Turiel, D., … Escalona, A. (2013). Baseline glycated hemoglobin levels are associated with duodenal-jejunal bypass liner-induced weight loss in obese patients. Surgical Endoscopy, 28(4), 1056-1062. doi:10.1007/s00464-013-3283-yOgata, H., Tokuyama, K., Nagasaka, S., Ando, A., Kusaka, I., Sato, N., … Yamamoto, Y. (2007). Long-range Correlated Glucose Fluctuations in Diabetes. Methods of Information in Medicine, 46(02), 222-226. doi:10.1055/s-0038-1625411Rodríguez de Castro, C., Vigil, L., Vargas, B., García Delgado, E., García Carretero, R., Ruiz-Galiana, J., & Varela, M. (2016). Glucose time series complexity as a predictor of type 2 diabetes. Diabetes/Metabolism Research and Reviews, 33(2), e2831. doi:10.1002/dmrr.2831DeFronzo, R. A. (2004). Pathogenesis of type 2 diabetes mellitus. Medical Clinics of North America, 88(4), 787-835. doi:10.1016/j.mcna.2004.04.013Zhang, X.-S., Roy, R. J., & Jensen, E. W. (2001). EEG complexity as a measure of depth of anesthesia for patients. IEEE Transactions on Biomedical Engineering, 48(12), 1424-1433. doi:10.1109/10.966601Bandt, C., & Pompe, B. (2002). Permutation Entropy: A Natural Complexity Measure for Time Series. Physical Review Letters, 88(17). doi:10.1103/physrevlett.88.174102Bian, C., Qin, C., Ma, Q. D. Y., & Shen, Q. (2012). Modified permutation-entropy analysis of heartbeat dynamics. Physical Review E, 85(2). doi:10.1103/physreve.85.021906Zhao, L., Wei, S., Zhang, C., Zhang, Y., Jiang, X., Liu, F., & Liu, C. (2015). Determination of Sample Entropy and Fuzzy Measure Entropy Parameters for Distinguishing Congestive Heart Failure from Normal Sinus Rhythm Subjects. Entropy, 17(12), 6270-6288. doi:10.3390/e17096270Weinstein, R. L., Schwartz, S. L., Brazg, R. L., Bugler, J. R., Peyser, T. A., & McGarraugh, G. V. (2007). Accuracy of the 5-Day FreeStyle Navigator Continuous Glucose Monitoring System: Comparison with frequent laboratory reference measurements. Diabetes Care, 30(5), 1125-1130. doi:10.2337/dc06-1602Weber, C., & Schnell, O. (2009). The Assessment of Glycemic Variability and Its Impact on Diabetes-Related Complications: An Overview. Diabetes Technology & Therapeutics, 11(10), 623-633. doi:10.1089/dia.2009.0043Cuesta-Frau, D., Miró-Martínez, P., Oltra-Crespo, S., Jordán-Núñez, J., Vargas, B., González, P., & Varela-Entrecanales, M. (2018). Model Selection for Body Temperature Signal Classification Using Both Amplitude and Ordinality-Based Entropy Measures. Entropy, 20(11), 853. doi:10.3390/e20110853Cuesta–Frau, D., Miró–Martínez, P., Oltra–Crespo, S., Jordán–Núñez, J., Vargas, B., & Vigil, L. (2018). Classification of glucose records from patients at diabetes risk using a combined permutation entropy algorithm. Computer Methods and Programs in Biomedicine, 165, 197-204. doi:10.1016/j.cmpb.2018.08.018Cuesta–Frau, D., Varela–Entrecanales, M., Molina–Picó, A., & Vargas, B. (2018). Patterns with Equal Values in Permutation Entropy: Do They Really Matter for Biosignal Classification? Complexity, 2018, 1-15. doi:10.1155/2018/1324696Mayer, C. C., Bachler, M., Hörtenhuber, M., Stocker, C., Holzinger, A., & Wassertheurer, S. (2014). Selection of entropy-measure parameters for knowledge discovery in heart rate variability data. BMC Bioinformatics, 15(S6). doi:10.1186/1471-2105-15-s6-s2Sheng Lu, Xinnian Chen, Kanters, J. K., Solomon, I. C., & Chon, K. H. (2008). Automatic Selection of the Threshold Value $r$ for Approximate Entropy. IEEE Transactions on Biomedical Engineering, 55(8), 1966-1972. doi:10.1109/tbme.2008.919870Crenier, L., Lytrivi, M., Van Dalem, A., Keymeulen, B., & Corvilain, B. (2016). 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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

Enabling the quantification of human stress from physiological responses

The stress response primes the human body to overcome a challenge or threat, and is governed by the sympathetic (SNS) and parasympathetic nervous systems (PNS). However, there is a great need to objectively identify stress, as overactivation of the SNS is dangerous to health. This PhD aimed to produce a heart rate variability (HRV) based measure of stress, to outperform the state-of-the- art and objectively identify levels of stress. Classification Angle (ClassA), a nonlinear framework based on second-order-difference plots of HRV, was developed to achieve this aim; ClassA produces four metrics, which include measures of SNS and PNS dynamics, and was tested on the HRV of ten males and ten females, exposed to periods of rest and mental (arithmetic) stress. The framework was compared to the state-of-the-art temporal, spectral and nonlinear measures. Only the heart rate of the males produced significant differences (p-value < 0.01) between the HRV from the rest and stress epochs. In contrast, two of the ClassA metrics were able to discern stress in both the males and females, with three-dimensional plots of the ClassA metrics spatially separating the HRV from the rest and stress epochs. The ability of the framework to distinguish between levels of stress was then tested on the cardiac signals of 28 day-surgery patients, recorded before and during surgery. Pain was defined as a stressor, and the ClassA framework produced significant differences between the HRV from the patients who experienced extreme pain, and those who did not. Furthermore, the ClassA framework has proved unique in accounting for sex-differences in the stress response, and in analysing only ten seconds of HRV data, whilst the state-of-the-art require five minutes. The development of the ClassA framework therefore achieves the PhD aim, and is the first step in the creation of a tool to objectively identify stress.Open Acces

A Novel Multivariate Sample Entropy Algorithm for Modeling Time Series Synchronization

Approximate and sample entropy (AE and SE) provide robust measures of the deterministic or stochastic content of a time series (regularity), as well as the degree of structural richness (complexity), through operations at multiple data scales. Despite the success of the univariate algorithms, multivariate sample entropy (mSE) algorithms are still in their infancy and have considerable shortcomings. Not only are existing mSE algorithms unable to analyse within- and cross-channel dynamics, they can counter-intuitively interpret increased correlation between variates as decreased regularity. To this end, we first revisit the embedding of multivariate delay vectors (DVs), critical to ensuring physically meaningful and accurate analysis. We next propose a novel mSE algorithm and demonstrate its improved performance over existing work, for synthetic data and for classifying wake and sleep states from real-world physiological data. It is furthermore revealed that, unlike other tools, such as the correlation of phase synchrony, synchronized regularity dynamics are uniquely identified via mSE analysis. In addition, a model for the operation of this novel algorithm in the presence of white Gaussian noise is presented, which, in contrast to the existing algorithms, reveals for the first time that increasing correlation between different variates reduces entropy

A Pilot Study of Heart Rate Variability Synchrony as a Marker of Intraoperative Surgical Teamwork and Its Correlation to the Length of Procedure

Objective: Quality of intraoperative teamwork may have a direct impact on patient outcomes. Heart rate variability (HRV) synchrony may be useful for objective assessment of team cohesion and good teamwork. The primary aim of this study was to investigate the feasibility of using HRV synchrony in surgical teams. Secondary aims were to investigate the association of HRV synchrony with length of procedure (LOP), complications, number of intraoperative glitches and length of stay (LOS). We also investigated the correlation between HRV synchrony and team familiarity, pre- and intraoperative stress levels (STAI questionnaire), NOTECHS score and experience of team members. Methods: Ear, nose and throat (ENT) and vascular surgeons (consultant and registrar team members) were recruited into the study. Baseline demographics including level of team members’ experience were gathered before each procedure. For each procedure, continuous electrocardiogram (ECG) recording was performed and questionnaires regarding pre- and intraoperative stress levels and non-technical skills (NOTECHS) scores were collected for each team member. An independent observer documented the time of each intraoperative glitch. Statistical analysis was conducted using stepwise multiple linear regression. Results: Four HRV synchrony metrics which may be markers of efficient surgical collaboration were identified from the data: 1. number of HRV synchronies per hour of procedure, 2. number of HRV synchrony trends per hour of procedure, 3. length of HRV synchrony trends per hour of procedure, 4. area under the HRV synchrony trend curve per hour of procedure. LOP was inversely correlated with number of HRV synchrony trends per hour of procedure (p p = 0.001), length of HRV synchrony trends per hour of procedure (p = 0.002) and number of HRV synchronies per hour of procedure (p p = 0.043; R = 0.358) and intraoperative STAI score of the whole team (p = 0.007; R = 0.493). Conclusions: HRV synchrony metrics within operating teams may be used as an objective marker to quantify surgical teamwork. We have shown that LOP is shorter when the intraoperative surgical teams’ HRV is more synchronised

The experimental protocol.

<p>The timeline was designed for collecting physiological data from the participants experiencing the low- and high-stress conditions.</p

Analysis framework from recorded ECG to the analysis of time, frequency and nonlinear performance metrics.

<p>MF-HT stands for the combination of matched filter and Hilbert transform algorithms, while PP and P respectively designate the periods pre- and during performance.</p

Stage call: Cardiovascular reactivity to audition stress in musicians - Fig 6

<p><b>Box plots of the normalized LF powers from male participants (top left), normalized HF powers from male participants (bottom left), normalized LF powers from female participants (top right), and normalized HF powers from female participants (bottom right).</b> The red horizontal marker indicates the median.</p

Statistical tests of frequency and nonlinear metrics.

<p>Statistical tests of frequency and nonlinear metrics.</p