Chronic fatigue syndrome with history of severe infection combined altered blood oxidant status, and reduced potassium efflux and muscle excitability at exercise

International audienceIt is documented that chronic fatigue syndrome (CFS) combines enhanced oxidative stress with altered muscle excitability. We hypothesized that these disorders may be accentuated when severe infection preceded the CFS symptoms. This case-control study compared 55 CFS patients to a matched control group of 40 healthy subjects. In twenty-five CFS patients, severe infection was reported within the three to seven month period preceding the CFS symptoms. The others had practiced sport at high level. Plasma concentrations of potassium, a marker of lipid peroxidation (thio-barbituric acid reactive substances, TBARS), and an endogenous antioxidant (reduced ascorbic acid, RAA) were measured. Action potential (M-wave) was evoked in the vastus lateralis muscle to explore the muscle membrane excitability. All subjects performed a maximal incremental cycling exercise. Compared to control subjects, all CFS patients presented an elevated resting TBARS level and, during and after exercise, an altered M-wave configuration. History of infection was associated with marked significant increase in resting TBARS level, enhanced M-wave alterations, and also reduced exercise-induced potassium efflux. The magnitude of exercise-induced M-wave alterations was proportional to the baseline TBARS level. Severe infection preceding CFS seems to constitute a stressor inducing altered blood oxidant status and a reduced muscle excitability at work

Mental Workload Alters Heart Rate Variability, Lowering Non-linear Dynamics

International audienceMental workload is known to alter cardiovascular function leading to increased cardiovascular risk. Nevertheless, there is no clear autonomic nervous system unbalance to be quantified during mental stress. We aimed to characterize the mental workload impact on the cardiovascular function with a focus on heart rate variability (HRV) non-linear indexes. A 1-h computerized switching task (letter recognition) was performed by 24 subjects while monitoring their performance (accuracy, response time), electrocardiogram and blood pressure waveform (finger volume clamp method). The HRV was evaluated from the beat-to-beat RR intervals (RRI) in time-, frequency-, and informational-domains, before (Control) and during the task. The task induced a significant mental workload (visual analog scale of fatigue from 27 ± 26 to 50 ± 31 mm, p < 0.001, and NASA-TLX score of 56 ± 17). The heart rate, blood pressure and baroreflex function were unchanged, whereas most of the HRV parameters markedly decreased. The maximum decrease occurred during the first 15 min of the task (P1), before starting to return to the baseline values reached at the end of the task (P4). The RRI dimension correlation (D2) decrease was the most significant (P1 vs. Control: 1.42 ± 0.85 vs. 2.21 ± 0.8, p < 0.001) and only D2 lasted until the task ended (P4 vs. Control: 1.96 ± 0.9 vs. 2.21 ± 0.9, p < 0.05). D2 was identified as the most robust cardiovascular variable impacted by the mental workload as determined by posterior predictive simulations (p = 0.9). The Spearman correlation matrix highlighted that D2 could be a marker of the generated frustration (R =-0.61, p < 0.01) induced by a mental task, as well as the myocardial oxygen consumption changes assessed by the double product (R =-0.53, p < 0.05). In conclusion, we showed that mental workload sharply lowered the non-linear RRI dynamics, particularly the RRI correlation dimension

Muscle metaboreflex activation during hypercapnia modifies nonlinear heart rhythm dynamics, increasing the complexity of the sinus node autonomic regulation in humans

International audienceMuscle metaboreflex activation during hypercapnia leads to enhanced pressive effects that are poorly understood while autonomic responses including baroreflex function are not documented. Thus, we assessed heart rate variability (HRV) that is partly due to autonomic influences on sinus node with linear tools (spectral analysis of instantaneous heart period), baroreflex set point and sensitivity with the heart period–arterial pressure transfer function and sequences methods, and system coupling through the complexity of RR interval dynamics with nonlinear tools (Poincaré plots and approximate entropy (ApEn)). We studied ten healthy young men at rest and then during muscle metaboreflex activation (MMA, postexercise muscle ischemia) and hypercapnia (HCA, PetCO2 =  + 10 mmHg from baseline) separately and combined (MMA + HCA). The strongest pressive responses were observed during MMA + HCA, while baroreflex sensitivity was similarly lowered in the three experimental conditions. HRV was significantly different in MMA + HCA compared to MMA and HCA separately, with the lowest total power spectrum (p < 0.05), including very low frequency (p < 0.05), low frequency (p < 0.05), and high frequency (tendency) power spectra decreases, and the lowest Poincaré plot short-term variability index (SD1): SD1 = 36.2 ms (MMA + HCA) vs. SD1 = 43.1 ms (MMA, p < 0.05) and SD1 = 46.1 ms (HCA, p < 0.05). Moreover, RR interval dynamic complexity was significantly increased only in the MMA + HCA condition (ApEn increased from 1.04 ± 0.04, 1.07 ± 0.02, and 1.05 ± 0.03 to 1.10 ± 0.03, 1.13 ± 0.04, and 1.17 ± 0.03 in MMA, HCA, and MMA + HCA conditions, respectively; p < 0.01). These results suggest that in healthy young men, muscle metaboreflex activation during hypercapnia leads to interactions that reduce parasympathetic influence on the sinus node activity but complexify its dynamics

Ultrasound assessment of the respiratory system using diaphragm motion-volume indices

International audienceBackground Although previous studies have determined limit values of normality for diaphragm excursion and thickening, it would be beneficial to determine the normal diaphragm motion-to-inspired volume ratio that integrates the activity of the diaphragm and the quality of the respiratory system. Methods To determine the normal values of selected ultrasound diaphragm motion-volume indices, subjects with normal pulmonary function testing were recruited. Ultrasound examination recorded diaphragm excursion on both sides during quiet breathing and deep inspiration. Diaphragm thickness was also measured. The inspired volumes of the corresponding cycles were systematically recorded using a spirometer. The indices were calculated using the ratio excursion, or percentage of thickening, divided by the corresponding breathing volume. From this corhort, normal values and limit values for normality were determined. These measurements were compared to those performed on the healthy side in patients with hemidiaphragm paralysis because an increase in hemidiaphragm activity has been previously demonstated in such circumstances. Results A total of 122 subjects (51 women, 71 men) with normal pulmonary function were included in the study. Statistical analysis revealed that the ratio of excursion, or percentage of thickening, to inspired volume ratio significantly differed between males and females. When the above-mentioned indices using excursion were normalized by body weight, no gender differences were found. The indices differed between normal respiratory function subjects and patients with hemidiaphragm paralysis (27 women, 41 men). On the paralyzed side, the average ratio of the excursion divided by the inspired volume was zero. On the healthy side, the indices using the excursion and the percentage of thickening during quiet breathing or deep inspiration were significantly increased comparedto patients with normal lung function. According to the logistic regression analysis, the most relevant indice appeared to be the ratio of the excursion measured during quiet breathing to the inspired volume. Conclusion The normal values of the diaphragm motion-volume indices could be useful to estimate the performance of the respiratory system. Proposed indices appear suitable in a context of hyperactivity

Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data

International audienceAtrial fibrillation (AF) is still a major cause of disease morbidity and mortality, making its early diagnosis desirable and urging researchers to develop efficient methods devoted to automatic AF detection. Till now, the analysis of Holter-ECG recordings remains the gold-standard technique to screen AF. This is usually achieved by studying either RR interval time series analysis, P-wave detection or combinations of both morphological characteristics. After extraction and selection of meaningful features, each of the AF detection methods might be conducted through univariate and multivariate data analysis. Many of these automatic techniques have been proposed over the last years. This work presents an overview of research studies of AF detection based on RR interval time series. The aim of this paper is to provide the scientific community and newcomers to the field of AF screening with a resource that presents introductory concepts, clinical features, and a literature review that describes the techniques that are mostly followed when RR interval time series are used for accurate detection of AF

An Efficient Pattern Recognition Kernel-Based Method for Atrial Fibrillation Diagnosis

International audienceThe aim of this work is to develop an efficient diagnosis method for atrial fibrillation (AF) arrhythmia based on inter-beat interval time series analysis and relevance vector machine (RVM) classifier. Automatic and fast AF diagnosis is still a major concern for the healthcare professional. Several algorithms based on univariate and multivariate analysis have been developed to detect AF. The published results do not show satisfactory detection accuracy especially for brief duration as short as one minute. Although RVM has been applied on tasks such as computer vision, natural language processing, speech recognition etc., this is the first attempt to adopt RVM for AF diagnosis. Four publicly-accessible sets of clinical data (AF Termination Challenge Database, MIT-BIH AF, Normal Sinus Rhythm RR Interval Database, and MIT-BIH Normal Sinus Rhythm Databases) were used for assessment. All time series were segmented in 1 min RR interval window and then three specific features were calculated. The RVM classifier was trained on 2000 randomly selected samples from the merged database. The results showed that the RVM model performed better than do existing algorithms, with 99.20% for both sensitivity and specificity

Relevance Vector Machine as Data-Driven Method for Medical Decision Making

International audienceThe aim of this work is to develop an efficient data-driven method for automatic medical decision making, especially for cardiac arrhythmia diagnosis. To achieve this goal, we have targeted the most common arrhythmia worldwide -atrial fibrillation (AF). Most of reported studies are dealing with inter-beat interval time series analysis coupled with univariate and/or multivariate data-driven methods. The state of the art of this subject revealed that although satisfactory detection findings have been achieved for long AF durations, there is still scope for improvement which needs to be addressed for brief episodes which is highly desired by healthcare professionals. Relevance vector machine (RVM) has been developed to address this issue. Several kernel functions and parameters have been tested to optimize RVM. Five geometrical and nonlinear features were extracted from 30s inter-beat time series. The RVM classifier was trained on 3000 randomly selected samples from four publicly-accessible sets of clinical data and tested on 1000 samples. The performance of the diagnosis model was evaluated by 10-fold cross-validation method. The results showed that the RVM model performed better than do existing algorithms, with 96.58% success rate. The automatic diagnosis on another dataset of 118985 samples of AF and Normal Sinus Rhythm (NSR) has yield 96.64% of classification accuracy. This automated data-driven decision making approach can be exploited for medical diagnosis of other arrhythmias

Comparison between standing and adjusted heights in Senegalese women

International audienc

Comparison between standing and adjusted heights in Senegalese women

International audienc