Using the heart rate variability for classifying patients with and without chronic heart failure and periodic breathing

Assessment of the dynamic interactions between cardiovascular signals can provide valuable information that improves the understanding of cardiovascular control. Heart rate variability (HRV) analysis is known to provide information about the autonomic heart rate modulation mechanism. Using the HRV signal, we aimed to obtain parameters for classifying patients with and without chronic heart failure (CHF), and with periodic breathing (PB), non-periodic breathing (nPB), and Cheyne-Stokes respiration (CSR) patterns. An electrocardiogram (ECG) and a respiratory flow signal were recorded in 36 elderly patients: 18 patients with CHF and 18 patients without CHF. According to the clinical criteria, the patients were classified into the follow groups: 19 patients with nPB pattern, 7 with PB pattern, 4 with Cheyne-Stokes respiration (CSR), and 6 non-classified patients (problems with respiratory signal). From the HRV signal, parameters in the time and frequency domain were calculated. Frequency domain parameters were the most discriminant in comparisons of patients with and without CHF: PTOT, PLF and fpHF. For the comparison of the nPB vs CSR patients groups, the best parameters were RMSSD and SDSD. Therefore, the parameters appear to be suitable for enhanced diagnosis of decompensated CHF patients and the possibility of developed periodic breathing and a CSR pattern

Analysis of Heart Rate Variability Using Time-Varying Filtering of Heart Transplanted Patients

International audienceIn this paper, we analyze the heart rate variability (HRV), obtained by using the time-varying integral pulse frequency modulation (TVIPFM) which is well adapted to the exercise stress testing. We consider that the mean heart period is varying function of time, during exercise. This technique allows the estimation of the autonomic nervous system modulation (ANS) from the beat occurrences. The estimated respiratory sinus arrhythmia is then filtered in the time-frequency domain around the respiration using a time-varying filter. It is proven that the Spectrogram is a convenient time-frequency representation that allows the implementation of such filter. The recorded data comes from exercise test performed by ten heart transplant patients. The magnitude of the filtered modulation of the heart rate due to respiration is compared to the date of transplantation taking into account the volume of respiration. It reveals that the normalized magnitude of the filtered variability, is significantly increased as the age of transplantation is higher with a high correlation coefficient (R=0.74, p=0.01). This correlation raised to 0.82 when considering dynamic behavior of the parameters. Applied to our dataset, standard parameter fails to exhibit such correlation

Heart rate vs stress indicator for short term mental stress

Heart rate variation (HR) being identified as depending on subjects’ stress state when submitted to short term mental stress, this study aimed at analyzing whether or not it could be possible to find a mathematical relationship between the average heart rate variation and the intensity S of a stress indicator in case of short term mental stress, whatever the stress indicator is. The method consisted in working the hypothesis by gathering data providing HR and ratio of frequency power of HRV (Heart Rate Variability) for different level of stress, HRV being considered as a stress indicator and presenting the advantage of being widely used in studies, therefore providing numerous data in the literature. From this data, a mathematical model was designed and then assessed by testing its reliability when applied to HR variation versus different types of stress indicators (EMG, GSR, Work Load, questionnaires such as STAI-S, ALES). The correlation obtained between the model and the data provided by the literature (24 points from 8 studies gathering 272 subjects) gave r=.95 (p<.0001) which allowed us to validate the model. Limits of the model were identified and discussed

Characterization of anesthetists’ behavior during simulation training: performance versus stress achieving medical tasks with or without physical effort

Decades of research about stress have shown that it could be source of performance but also of cognitive deficit. The studies have led to highlight occupational stress variables that researchers have characterized by physiological measurements, data treatments and protocols becoming more and more complex with time. If these devices are gaining in precision, they are now too complex to allow non-specialist users to produce a quick interpretation of results. Yet for vocational training, specifically on simulators, trainers need to know in real time whether or not what they implement allows the trainees to learn in good conditions, i.e. by favoring the behavior produced by the positive effect of stress on performance. The present paper addresses the performance versus occupational stress during training sessions of anesthetists on simulator. We studied the performance and stress with or without physical effort using a simple protocol based on the use of basic heart parameters in order to obtain a quasi-instantaneous interpretation of the data. We identified cognitive deficit zone during training according to the Yerkes & Dodson (1908) relationship between performance and stress. We showed that performance versus stress during simulation training with or without physical efforts could be successfully analyzed for immediate assessment of stress influencing performance. Suggestions have been made for improving training sessions and avoid trainees’ behavior induced by cognitive deficit. Limits of the protocol are exposed

Strengthening of prism beam by using NSM technique with roots planted in concrete

This paper presents experimental results of four prismatic concrete reinforced beam and strengthened by NSM (Near surface mounted) FRP (Fiber Reinforced Polymer) reinforced technique, with additional roots planted in the concrete. The strengthening technique causes load capacity of beams to increase from (6%-8%).A decrease in mid-span deflection was also observed from (4%-5%).Using this technique gave increasing in flexural beam resistant under the same conditions and this increasing was also noted in shear beam resistant

Study of the ANS Sympathovagal Behavior Using the ReliefF and the KS-Segmentation Algorithms

The Autonomous Nervous System (ANS) sympathovagal balance was studied using several features derived from Heart Rate Variability signals (HRV). The HRV signals are, however naturally, non-stationary since their statistical properties vary under time transition. A useful approach to quantifying them is, therefore, to consider them as consisting of some intervals that are themselves stationary. To obtain the latter, we have applied the so called the KS-segmentation algorithm which is an approach deduced from the Kolmogorov-Smirnov (KS) statistics. To determine, accurately, these features, we have used the ReliefF algorithm which is one of the most successful strategies in feature selection; this step allows us to select the most relevant features from thirty three features at the beginning. As result the ratio between LF and HF band powers of HRV signal, the Standard Deviation of RR intervals (SDNN), and Detrended Fluctuation Analysis with Short term slope (DFA &#945;1), are more accurate for each stationary segment, and present the best results comparing with other features for the classification of the three stages of stress in real world driving tasks (Low, Medium and High stress).Bouziane, A.; Yagoubi, B.; Vergara Domínguez, L.; Salazar Afanador, A. (2015). Study of the ANS Sympathovagal Behavior Using the ReliefF and the KS-Segmentation Algorithms. WSEAS Transactions on Biology and Biomedicine. 12:8-15. http://hdl.handle.net/10251/65958S8151

Study of the ANS Sympathovagal Behavior Using the ReliefF and the KS-Segmentation Algorithms

The Autonomous Nervous System (ANS) sympathovagal balance was studied using several features derived from Heart Rate Variability signals (HRV). The HRV signals are, however naturally, non-stationary since their statistical properties vary under time transition. A useful approach to quantifying them is, therefore, to consider them as consisting of some intervals that are themselves stationary. To obtain the latter, we have applied the so called the KS-segmentation algorithm which is an approach deduced from the Kolmogorov-Smirnov (KS) statistics. To determine, accurately, these features, we have used the ReliefF algorithm which is one of the most successful strategies in feature selection; this step allows us to select the most relevant features from thirty three features at the beginning. As result the ratio between LF and HF band powers of HRV signal, the Standard Deviation of RR intervals (SDNN), and Detrended Fluctuation Analysis with Short term slope (DFA &#945;1), are more accurate for each stationary segment, and present the best results comparing with other features for the classification of the three stages of stress in real world driving tasks (Low, Medium and High stress).Bouziane, A.; Yagoubi, B.; Vergara Domínguez, L.; Salazar Afanador, A. (2015). Study of the ANS Sympathovagal Behavior Using the ReliefF and the KS-Segmentation Algorithms. WSEAS Transactions on Biology and Biomedicine. 12:8-15. http://hdl.handle.net/10251/65958S8151

Autonomic nervous system measurement in hyperbaric environments using ECG and PPG signals

The main aim of this work was to characterise the Autonomic Nervous System (ANS) response in hyper- baric environments using electrocardiogram (ECG) and pulse- photoplethysmogram (PPG) signals. To that end, 26 subjects were introduced into a hyperbaric chamber and five stages with different atmospheric pressures (1 atm; descent to 3 and 5 atm; ascent to 3 and 1 atm) were recorded. Respiratory information was extracted from the ECG and PPG signals and a combined respiratory rate was studied. This information was also used to analyse Heart Rate Variability (HRV) and Pulse Rate Variability (PRV). The database was cleaned by eliminating those cases where the respiratory rate dropped into the low frequency band (LF: 0.04-0.15 Hz) and those in which there was a discrepancy between the respiratory rates estimated using the ECG and PPG signals. Classical temporal and frequency indices were calculated in such cases. The ECG results showed a time-related depen- dency, with the heart rate and sympathetic markers (normalised power in LF and LF/HF ratio) decreasing as more time was spent inside the hyperbaric environment. A dependency between the atmospheric pressure and the parasympathetic response, as reflected in the high frequency band power (HF: 0.15-0.40 Hz), was also found, with power increasing with atmospheric pressure. The combined respiratory rate also reached a maximum in the deepest stage, thus highlighting a significant difference between this stage and the first one. The PPG data gave similar findings and also allowed the oxygen saturation to be computed, therefore we propose the use of this signal for future studies in hyperbaric environments

Autonomic Nervous System characterization in hyperbaric environments considering respiratory component and non-linear analysis of Heart Rate Variability

Objectives: an evaluation of Principal Dynamic Mode (PDM) and Orthogonal Subspace Projection (OSP) methods to characterize the Autonomic Nervous System (ANS) response in three different hyperbaric environments was performed. Methods: ECG signals were recorded in two different stages (baseline and immersion) in three different hyperbaric environments: (a) inside a hyperbaric chamber, (b) in a controlled sea immersion, (c) in a real reservoir immersion. Time-domain parameters were extracted from the RR series of the ECG. From the Heart Rate Variability signal (HRV), classic Power Spectral Density (PSD), PDM (a non-linear analysis of HRV which is able to separate sympathetic and parasympathetic activities) and OSP (an analysis of HRV which is able to extract the respiratory component) methods were used to assess the ANS response. Results: PDM and OSP parameters follows the same trend when compared to the PSD ones for the hyperbaric chamber dataset. Comparing the three hyperbaric scenarios, significant differences were found: i) heart rate decreased and RMSSD increased in the hyperbaric chamber and the controlled dive, but they had the opposite behavior during the uncontrolled dive; ii) power in the OSP respiratory component was lower than power in the OSP residual component in cases a and c; iii) PDM and OSP methods showed a significant increase in sympathetic activity during both dives, but parasympathetic activity increased only during the uncontrolled dive. Conclusions: PDM and OSP methods could be used as an alternative measurement of ANS response instead of the PSD method. OSP results indicate that most of the variation in the heart rate variability cannot be described by changes in the respiration, so changes in ANS response can be assigned to other factors. Time-domain parameters reflect vagal activation in the hyperbaric chamber and in the controlled dive because of the effect of pressure. In the uncontrolled dive, sympathetic activity seems to be dominant, due to the effects of other factors such as physical activity, the challenging environment, and the influence of breathing through the scuba mask during immersion. In sum, a careful description of the changes in all the possible factors that could affect the ANS response between baseline and immersion stages in hyperbaric environments is needed for better interpretation of the results