ABSTRACT
BACKGROUND: Our aim was to determine the differences in short-term heart rate variability (HRV) between patients with metabolic syndrome (MS) and healthy controls. METHODS: We searched electronic databases for primary works with short-term HRV recordings (≤30 min) that made comparisons between individuals with MS versus healthy controls. This systematic review and meta-analysis (MA) was performed according to PRISMA guidelines and registered at PROSPERO (CRD42022358975). RESULTS: Twenty-eight articles were included in the qualitative synthesis and nineteen met the criteria for the MA. Patients with MS showed decreased SDNN (-0.36 [-0.44, -0.28], p < 0.001), rMSSD (-7.59 [-9.98, -5.19], p < 0.001), HF (-0.36 [-0.51, -0.20], p < 0.00001) and LF (-0.24 [-0.38, -0.1], p = 0.001). In subsequent subanalyses, we found a decrease in SDNN (-0.99 (-1.45, -0.52], p < 0.001), rMSSD (-10.18 [-16.85, -3.52], p < 0.01) and HF (-1.04 [-1.97, -0.1] p < 0.05) in women. In men, only LF showed a significant lower value (-0.26 [-0.5, -0.02], p < 0.05). We could not perform MA for non-linear variables. CONCLUSIONS: Patients with MS showed changes in time-domain analyses, with lower values in SDNN and rMSSD. Regarding frequency-domain analyses, MS patients showed a decrease in HF and LF When sex was used as a grouping variable, the MA was only possible in one of both sexes (men or women) in rMSSD and LF/HF. Lastly, when data for both men and women were available, subanalyses showed a different behavior compared to mixed analyses for SDNN, HF and LF, which might point towards a different impact of MS in men and women.
ABSTRACT
BACKGROUND: Our aim was to determine the impact that metabolic syndrome (MS) produces in long-term heart rate variability (HRV), quantitatively synthesizing the results of published studies to characterize the cardiac autonomic dysfunction in MS. METHODS: We searched electronic databases for original research works with long-term HRV recordings (24 h) that compared people with MS (MS+) versus healthy people as a control group (MS-). This systematic review and meta-analysis (MA) was performed according to PRISMA guidelines and registered at PROSPERO (CRD42022358975). RESULTS: A total of 13 articles were included in the qualitative synthesis, and 7 of them met the required criteria to be included in the MA. SDNN (-0.33 [-0.57, 0.09], p = 0.008), LF (-0.32 [-0.41, -0.23], p < 0.00001), VLF (-0.21 [-0.31, -0.10], p = 0.0001) and TP (-0.20 [-0.33, -0.07], p = 0.002) decreased in patients with MS. The rMSSD (p = 0.41), HF (p = 0.06) and LF/HF ratio (p = 0.64) were not modified. CONCLUSIONS: In long-term recordings (24 h), SDNN, LF, VLF and TP were consistently decreased in patients with MS. Other parameters that could be included in the quantitative analysis were not modified in MS+ patients (rMSSD, HF, ratio LF/HF). Regarding non-linear analyses, the results are not conclusive due to the low number of datasets found, which prevented us from conducting an MA.
ABSTRACT
Internet of Things (IoT) technologies allow building a digital representation of people, objects, or physical phenomena to be available on the Internet. Thus, stakeholders can access this information from remote places or computational systems could analyze this data to find patterns, make decisions, or execute actions. For instance, a doctor could diagnose patients by analyzing the received data from an IoT system even when patients are located in a remote place. This article proposes an IoT system for monitoring electrocardiogram (ECG) signal and processing heart data in order to generate an alert when an arrhythmia is present. This system involves a Polar H10 heart sensor, machine-learning models to classify heart events, and communication technology to share and store patient's information. In the first place, the architecture of the IoT monitoring system and the communication between the components are described by discussing the designing criteria. Second, the experimentation process performs the training and the assessment of three classification algorithms, random forest, convolutional neural network, and k-nearest neighbors. The results show that k-nearest neighbor has the best accuracy percentage classifying the arrhythmias under study (premature ventricular contraction 94%, fusion of ventricular beat 81%, and supraventricular premature beat 82%); also, it is able to discern normal and unclassifiable beats with 93% and 97%, respectively.
Subject(s)
Internet of Things , Ventricular Premature Complexes , Humans , Algorithms , Neural Networks, Computer , Machine LearningABSTRACT
Introducción: La variabilidad de la frecuencia cardíaca (VFC) permite estudiar de forma no invasiva la modulación autonómica de la función cardiovascular. Según el principio de especificidad del entrenamiento, cada tipo de ejercicio produce adaptaciones específicas. Sin embargo, no se ha establecido si este concepto también es aplicable a la VFC. El presente estudio tiene como objetivo comparar los componentes espectrales de la VFC en hombres jóvenes entrenados aeróbicamente y anaeróbicamente. Material y métodos: Estudio analítico descriptivo de corte transversal. Se analizaron los componentes espectrales de la VFC en reposo a partir de registros cortos en 12 corredores, 10 levantadores de peso y 11 sujetos control, no activos físicamente. Resultados: Los sujetos entrenados aeróbicamente presentaron los valores más bajos en el componente de baja frecuencia (BF) y los valores más altos en el de alta frecuencia (AF), pero estas diferencias no fueron estadísticamente significativas. El poder espectral total fue similar en todos los grupos (p=0,103), al igual que la relación del componente de BF dividido en el de AF (p=0,094). La frecuencia cardíaca en reposo en el grupo entrenado aeróbicamente fue significativamente menor con respecto al grupo entrenado anaeróbicamente (p<0,01) y al control (p<0,001). Conclusiones: Los resultados no mostraron efecto del entrenamiento físico regular a largo plazo, ya fuese aeróbico o anaeróbico, sobre los componentes espectrales de la VFC. La bradicardia en reposo observada en nuestros sujetos de estudio entrenados aeróbicamente, no se explica por cambios en el control autonómico de la función cardiovascular(AU)
Introduction: Heart rate variability (HRV) is a non-invasive tool for studying autonomic modulation of cardiovascular function. According to the specificity principle of training, each type of exercise causes specific adaptations. However, whether this concept also applies to HRV has not been established. The aim of this study was to compare the spectral components of HRV between endurance-trained and strength-trained young men. Material and methods: Cross sectional analytical descriptive study. Spectral components of HRV at rest were analysed from short records in 12 runners, 10 weight lifters and 11 not physically active control subjects. Results: Endurance-trained subjects showed the lowest values in the low frequency component (LF) and the highest values at high frequency (HF), but these differences were not statistically significant. Total spectral power was similar in all groups (P=.103), as well as the ratio of low frequency components divided into high frequency (LF/HF) (P=.094). Heart rate at rest in aerobically trained group was significantly lower compared to strength-trained group (P<.01) and controls (P<.001). Conclusions: The results showed no effect of long-term regular aerobic or anaerobic physical training, on spectral components of HRV. In our aerobically trained subjects, rest bradycardia was not explained by changes in the autonomic control of cardiovascular function(AU)
