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

Mental 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

Cardiopulmonary Response to Exercise in COPD and Overweight Patients: Relationship between Unloaded Cycling and Maximal Oxygen Uptake Profiles

Cardiopulmonary response to unloaded cycling may be related to higher workloads. This was assessed in male subjects: 18 healthy sedentary subjects (controls), 14 hypoxemic patients with chronic obstructive pulmonary disease (COPD), and 31 overweight individuals (twelve were hypoxemic). They underwent an incremental exercise up to the maximal oxygen uptake (VO 2 max), preceded by a 2 min unloaded cycling period. Oxygen uptake (VO 2 ), heart rate (HR), minute ventilation (VE), and respiratory frequency (fR) were averaged every 10 s. At the end of unloaded cycling period, HR increase was significantly accentuated in COPD and hypoxemic overweight subjects (resp., +14 ± 2 and +13 ± 1.5 min −1 , compared to +7.5 ± 1.5 min −1 in normoxemic overweight subjects and +8 ± 1.8 min −1 in controls). The fR increase was accentuated in all overweight subjects (hypoxemic: +4.5 ± 0.8; normoxemic: +3.9 ± 0.7 min −1 ) compared to controls (+2.5 ± 0.8 min −1 ) and COPDs (+2.0 ± 0.7 min −1 ). The plateau VE increase during unloaded cycling was positively correlated with VE values measured at the ventilatory threshold and VO 2 max. Measurement of ventilation during unloaded cycling may serve to predict the ventilatory performance of COPD patients and overweight subjects during an exercise rehabilitation program

Effects of lenten fasting on body composition and biochemical parameters

Background: The catholic lenten fasting is the period of 40 days of fasting that precedes Easter. It is one of religious fasting less documented in the scientific literature. Thus the aim of our study was to evaluate the evolution of anthropometric and body composition and biochemical profile during Catholic lenten fasting.Methods: We conducted a prospective study, which took place during the period between one week before at the end of lenten fasting. Eleven fasters (4 women and 7 men), aged between 18 and 59 years were included in present study. Anthropometric, body composition parameters and biochemical profile were evaluated one week before, at 15th day and at the end of Lenten fasting.Results: Weight, body mass index (BMI) and visceral fat decreased significantly at the end of Lenten fasting. Lipid profile changed significantly during this fasting period. Total cholesterol (TC), low density lipoprotein – cholesterol (LDL-C) and triglycerides decreased significantly with fasting. High density lipoprotein – cholesterol (HDL-C) was remained unchanged during this fasting period while TC/HDL ratio was significantly decreased at the end of Lent.Conclusions: Present study showed that the fasting of Lent seems to have beneficial effects on reducing cardiovascular risk factors. Further studies are required to better understand the physiological mechanisms involved for a therapeutic use

Oxygénation musculaire et contrôle sensori-moteur

AIX-MARSEILLE2-BU Méd/Odontol. (130552103) / SudocSudocFranceF

Facteurs de risque d'infections pulmonaires chez les traumatisés graves sous décontamination digestive sélective

AIX-MARSEILLE2-BU Méd/Odontol. (130552103) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Espèces réactives de l'oxygène et contrôle sensorimoteur musculaire

AIX-MARSEILLE2-BU Méd/Odontol. (130552103) / SudocSudocFranceF

Le syndrome du burnout (un "vrai" facteur de risque cardiovasculaire)

Le Burn-out est un syndrome d épuisement émotionnel, de dépersonnalisation et de réduction de l'accomplissement personnel chez des individus impliqués professionnellement auprès d'autrui. Le syndrome du burn-out peine à être reconnu comme une entité originale à coté d'autres nosologies comme les troubles de l'adaptation, le stress, le syndrome dépressif, l'anxiété. Le questionnaire MBI ( Maslach Burnout Inventory) est l'outil le plus utilisé pour quantifier le niveau de burn-out. Le syndrome de Burn-out (SBO) entraîne une augmentation de l'activité sympathique durant les tâches mentales, une diminution du rebond vagal et de la sensibilité un baroréflexe lors de la récupération cardiovasculaire. Le stress au travail est un facteur de risque cardiovasculaire indépendant. À contratrio, l'entraînement à la gestion des émotions augmenterait le tonus vagal et serait cardioprotecteurAIX-MARSEILLE2-BU Méd/Odontol. (130552103) / SudocSudocFranceF

A Literature Review: ECG-Based Models for Arrhythmia Diagnosis Using Artificial Intelligence Techniques

International audienceIn the health care and medical domain, it has been proven challenging to diagnose correctly many diseases with complicated and interferential symptoms, including arrhythmia. However, with the evolution of artificial intelligence (AI) techniques, the diagnosis and prognosis of arrhythmia became easier for the physicians and practitioners using only an electrocardiogram (ECG) examination. This review presents a synthesis of the studies conducted in the last 12 years to predict arrhythmia’s occurrence by classifying automatically different heartbeat rhythms. From a variety of research academic databases, 40 studies were selected to analyze, among which 29 of them applied deep learning methods (72.5%), 9 of them addressed the problem with machine learning methods (22.5%), and 2 of them combined both deep learning and machine learning to predict arrhythmia (5%). Indeed, the use of AI for arrhythmia diagnosis is emerging in literature, although there are some challenging issues, such as the explicability of the Deep Learning methods and the computational resources needed to achieve high performance. However, with the continuous development of cloud platforms and quantum calculation for AI, we can achieve a breakthrough in arrhythmia diagnosis

Cardiopulmonary Response to Exercise in COPD and Overweight Patients: Relationship between Unloaded Cycling and Maximal Oxygen Uptake Profiles

Cardiopulmonary response to unloaded cycling may be related to higher workloads. This was assessed in male subjects: 18 healthy sedentary subjects (controls), 14 hypoxemic patients with chronic obstructive pulmonary disease (COPD), and 31 overweight individuals (twelve were hypoxemic). They underwent an incremental exercise up to the maximal oxygen uptake (VO2max), preceded by a 2 min unloaded cycling period. Oxygen uptake (VO2), heart rate (HR), minute ventilation (VE), and respiratory frequency (fR) were averaged every 10 s. At the end of unloaded cycling period, HR increase was significantly accentuated in COPD and hypoxemic overweight subjects (resp., +14±2 and +13±1.5 min−1, compared to +7.5±1.5 min−1 in normoxemic overweight subjects and +8±1.8 min−1 in controls). The fR increase was accentuated in all overweight subjects (hypoxemic: +4.5±0.8; normoxemic: +3.9±0.7 min−1) compared to controls (+2.5±0.8 min−1) and COPDs (+2.0±0.7 min−1). The plateau VE increase during unloaded cycling was positively correlated with VE values measured at the ventilatory threshold and VO2max. Measurement of ventilation during unloaded cycling may serve to predict the ventilatory performance of COPD patients and overweight subjects during an exercise rehabilitation program

A filter approach for feature selection in classification: application to automatic atrial fibrillation detection in electrocardiogram recordings

International audienceBackground: In high-dimensional data analysis, the complexity of predictive models can be reduced by selecting the most relevant features, which is crucial to reduce data noise and increase model accuracy and interpretability. Thus, in the field of clinical decision making, only the most relevant features from a set of medical descriptors should be considered when determining whether a patient is healthy or not. This statistical approach known as feature selection can be performed through regression or classification, in a supervised or unsupervised manner. Several feature selection approaches using different mathematical concepts have been described in the literature. In the field of classification, a new approach has recently been proposed that uses the γ-metric, an index measuring separability between different classes in heart rhythm characterization. The present study proposes a filter approach for feature selection in classification using this γ-metric, and evaluates its application to automatic atrial fibrillation detection. Methods: The stability and prediction performance of the γ-metric feature selection approach was evaluated using the support vector machine model on two heart rhythm datasets, one extracted from the PhysioNet database and the other from the database of Marseille University Hospital Center, France (Timone Hospital). Both datasets contained electrocardiogram recordings grouped into two classes: normal sinus rhythm and atrial fibrillation. The performance of this feature selection approach was compared to that of three other approaches, with the first two based on the Random Forest technique and the other on receiver operating characteristic curve analysis. Results: The γ-metric approach showed satisfactory results, especially for models with a smaller number of features. For the training dataset, all prediction indicators were higher for our approach (accuracy greater than 99% for models with 5 to 17 features), as was stability (greater than 0.925 regardless of the number of features included in the model). For the validation dataset, the features selected with the y-metric approach differed from those selected with the other approaches; sensitivity was higher for our approach, but other indicators were similar. Conclusion: This filter approach for feature selection in classification opens up new methodological avenues for atrial fibrillation detection using short electrocardiogram recordings