Cardiovascular disease (CVD) is one of the leading causes of death worldwide. Heart sound classification plays a key role in the early detection of CVD. The difference between normal and abnormal heart sounds is not obvious. In this paper, in order to improve the accuracy of the heart sound classification model, we propose a heart sound feature extraction method based on bispectral analysis and combine it with convolutional neural network (CNN) to classify heart sounds. The model can effectively suppress Gaussian noise by using bispectral analysis and can effectively extract the features of heart sound signals without relying on the accurate segmentation of heart sound signals. At the same time, the model combines with the strong classification performance of convolutional neural network and finally achieves the accurate classification of heart sound. According to the experimental results, the proposed algorithm achieves 0.910, 0.884 and 0.940 in terms of accuracy, sensitivity and specificity under the same data and experimental conditions, respectively. Compared with other heart sound classification algorithms, the proposed algorithm shows a significant improvement and strong robustness and generalization ability, so it is expected to be applied to the auxiliary detection of congenital heart disease.
Objectives
To analyze the risk factors for cardiovascular disease (CVD) in patients with chronic obstructive pulmonary disease (COPD) of different severities.
Methods
The study included 50 patients with mild-to-moderate COPD and 50 with severe-to-very severe COPD admitted between January 2014 and January 2016. Comorbidities were recorded on the basis of data obtained from medical charts and clinical evaluations. The Charlson comorbidity index was calculated, and the Hospital Anxiety and Depression Scale (HADS) score was determined in each subject.
Results
There were more prevalences of smoking, depression and dyslipidemia in the patients with mild-to-moderate COPD than those with severe-to-very severe COPD (all P<0.001). The prevalences of high blood pressure, diabetes mellitus, alcoholism, and chronic heart failure were not different significantly between the two groups. The Charlson comorbidity index and HADS scores were not different between the two groups.
Conclusions
Comorbidities are fairly common in COPD regardless of its severity. Certain risk factors for CVD, as smoking, dyslipidemia, and depression, appear to be more prevalent in patients with mild-to-moderate COPD.
Cardiovascular diseases is the leading cause of threat to human life and health worldwide. Early risk assessment, timely diagnosis, and prognosis evaluation are critical to the treatment of cardiovascular diseases. Currently, the evaluation of diagnosis and prognosis of cardiovascular diseases mainly relies on imaging examinations such as coronary CT and coronary angiography, which are expensive, time-consuming, partly invasive, and require high professional competence of the operator, making it difficult to promote in the community or in areas where medical resources are scarce. The fundus microcirculation is a part of the human microcirculation and has similar embryological origins and physiopathological features to cardiovascular circulation. Several studies have revealed fundus imaging biomarkers associated with cardiovascular diseases, and developed and validated intelligent diagnosis and treatment models for cardiovascular diseases based on fundus imaging data. Fundus imaging is expected to be an important adjunct to cardiovascular disease diagnosis and treatment given its noninvasive and convenient nature. The purpose of this review is to summarize the current research status, challenges, and future prospects of the application of artificial intelligence based on multimodal fundus imaging data in cardiovascular disease diagnosis and treatment.
The main cause of death in patients with end-stage renal disease (ESRD) is cardiovascular disease, and trimethylamine-N-oxide (TMAO) has been found to be one of the specific risk factors in the pathogenic process in recent years. TMAO is derived from intestinal bacterial metabolism of dietary choline, carnitine and other substances and subsequently catalyzed by flavin monooxygenase enzymes in the liver. The changes of intestinal bacteria in ESRD patients have contributed to the accumulation of gut-derived uremic toxins such as TMAO, indoxyl sulfate and indole-3-acetic acid. While elevated TMAO concentration accelerates atherosclerosis through mechanisms such as inflammation, increased scavenger receptor expression, and inhibition of reverse cholesterol transport. In this review, this research introduces the biological function, metabolic processes of TMAO and mechanisms by which TMAO promotes the progression of cardiovascular disease in ESRD patients and summarizes current interventions that may be used to reverse gut microbiota disturbances, such as activated carbon, fecal microbial transplantation, dietary improvement, probiotic and probiotic introduction. It also focuses on exploring intervention targets to reduce the gut-derived uremic toxin TMAO in order to explore the possibility of more cardiovascular disease treatments for ESRD patients.
Cardiovascular disease has caused a huge burden of disease worldwide, and the rapid advancement of smart wearable devices has provided new means for early diagnosis, real-time monitoring, and event prevention of cardiovascular disease. Smart wearable devices can be classified into various categories based on detection signals and physical carrier types. Based on an overview of the composition of such devices, this article further introduces the current cutting-edge research and related market products related to smart blood pressure monitoring, electrocardiogram monitoring, and ultrasound monitoring. It also discusses the future development and challenges of such devices, aiming to provide evidence support for the research and development of smart wearable devices in the diagnosis and treatment of cardiovascular diseases in the future.
ObjectiveTo systematically evaluate the efficacy and safety of Polypill on cardiovascular risk factors.
MethodSuch databases as Embase, Cochrane Library, PubMed, Web of Science, China National Knowledge Infrastracture, WanFang, and China Biology Medicine Disc were searched from their establishment to May 2015 for randomized controlled trials on the efficacy and safety of Polypillon on cardiovascular risk factors. Meta-analyses were performed by using the RevMan 5.2 software.
ResultsSix studies were included which all came from overseas including 1 155 patients treated with Polypill and 1 149 treated with placebo or single or combined medicaiton. The meta-analysis showed that:when compared with the controls, Polypills could significantly reduce systolic blood pressure[WMD=-9.39 mm Hg (1 mm Hg=0.133 kPa), 95%CI (-14.44, -4.33) mm Hg, P=0.0003], diastolic blood pressure[WMD=-5.32 mm Hg, 95%CI (-8.10, -2.55) mm Hg, P=0.0002], total cholesterol[WMD=-1.11 mmol/L, 95%CI (-1.48, -0.74) mmol/L, P<0.00001], and low density lipoprotein[WMD=-0.91 mmol/L, 95%CI (-1.25, -0.57) mmol/L, P<0.00001]. However, those who took Polypill were easier to discontinue medication [OR=1.49, 95%CI (1.19, 1.87), P=0.0005]. In the respect of adverse effects, there was no significant difference between the two groups[OR=1.47, 95%CI (0.67, 3.25), P=0.34].
ConclusionsThe efficacy of Polypill on cardiovascular risk factors is obvious. Polypills can significantly reduce blood pressure and lipids. Tolerability is lower in those taking Polypills, but the difference is minor. There is no significant difference in the respect of adverse effects.
The peak period of cardiovascular disease (CVD) is around the time of awakening in the morning, which may be related to the surge of sympathetic activity at the end of nocturnal sleep. This paper chose 140 participants as study object, 70 of which had occurred CVD events while the rest hadn’t during a two-year follow-up period. A two-layer model was proposed to investigate whether hypnopompic heart rate variability (HRV) was informative to distinguish these two types of participants. In the proposed model, the extreme gradient boosting algorithm (XGBoost) was used to construct a classifier in the first layer. By evaluating the feature importance of the classifier, those features with larger importance were fed into the second layer to construct the final classifier. Three machine learning algorithms, i.e., XGBoost, random forest and support vector machine were employed and compared in the second layer to find out which one can achieve the highest performance. The results showed that, with the analysis of hypnopompic HRV, the XGBoost+XGBoost model achieved the best performance with an accuracy of 84.3%. Compared with conventional time-domain and frequency-domain features, those features derived from nonlinear dynamic analysis were more important to the model. Especially, modified permutation entropy at scale 1 and sample entropy at scale 3 were relatively important. This study might have significance for the prevention and diagnosis of CVD, as well as for the design of CVD-risk assessment system.
ObjectiveTo investigate the correlation between lipid accumulation product (LAP) and risk of ischemic cardiovascular disease (ICVD).
MethodsThis cross-sectional study was performed among community residents from an urban community in Chengdu area between September 2011 and June 2012. Questionnaire survey was carried out. Each individual underwent biochemistry analysis and physical examination. In addition, brachial-ankle pulse wave velocity (BaPWV) and augmentation index (AI) were detected. Pearson correlation analysis was performed to explore the relationship between LAP and each cardiovascular risk factor. Liner regression model was used to analyze the relationship between LAP and ICVD.
ResultsA total of 780 individuals with complete data were included in the analysis. LAP was correlated with blood pressure, total cholesterol, high density lipoprotein cholesterol, fasting blood glucose, and BaPWV (P＜0.05). LAP was associated with the risk of ICVD (r=0.253, P＜0.001). After being adjusted with sex, age and other cardiovascular risk factors, LAP was also correlated with the risk of ICVD (r=0.050, P＜0.001).
ConclusionsHigh LAP is associated with elevated cardiovascular risks and subclinical vascular damage. In addition, LAP is correlated with ICVD risk, thus it may be used to predict the incidence of ICVD to some extent. However, as the correlation is weak, our study does not support the direct use of this indicator to predict ICVD. Large-sample studies based on different races and ages are needed.
In recent years, the diversity of gut microbiota and the role of its metabolites in cardiovascular disease (CVD) have attracted widespread attention. Gut microbiota metabolites not only play an important role in maintaining gut health, but may also influence cardiovascular health through a variety of mechanisms. As one of the important products of gut microbiota metabolism, sulfate’s biosynthetic pathway, metabolic dynamics and potential effects on cardiovascular system have become the focus of research. However, the current research on the relationship between sulfate and cardiovascular disease still has some shortcomings, including the mechanism is not clear, and clinical data are limited. This article reviewed the biosynthesis of sulfate and its mechanism of action in cardiovascular diseases, and combined with the existing clinical research results, aimed to provide new perspectives and ideas for future research, in order to promote the in-depth exploration and development of this field.
There are various examination methods for cardiovascular diseases. Non-invasive diagnosis and prognostic information acquisition are the current research hotspots of related imaging examinations. Positron emission tomography (PET)/magnetic resonance imaging (MRI) is a new advanced fusion imaging technology that combines the molecular imaging of PET with the soft tissue contrast function of MRI to achieve their complementary advantages. This article briefly introduces several major aspects of cardiac PET/MRI in the diagnosis of cardiovascular disease, including atherosclerosis, ischemic cardiomyopathy, nodular heart disease, and myocardial amyloidosis, in order to promote cardiac PET/MRI to be more widely used in precision medicine in this field.
