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.
The World Health Organization (WHO) released the “Global report on hypertension” on September 19, 2023. This report systematically summarizes the prevalence, mortality, diagnosis and treatment of hypertension in various countries, and elucidates the current situation of hypertension management, and gives a series of suggestions on how to manage hypertension, providing new thinking and inspiration for countries to optimize hypertension management. Through the summary of relevant studies and reports, this paper further reviews the present situation, early identification and management of hypertension.
ObjectivesTo systematically review the efficacy of Nordic walking on prognosis of cardiovascular diseases. MethodsPubMed, Web of Science, EMbase, The Cochrane Library, CBM, CNKI and VIP databases were electronically searched to collect intervention studies on the efficacy of Nordic walking on prognosis of cardiovascular diseases from inception to June, 2018. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies, then, meta-analysis was performed by RevMan 5.3 software. ResultsA total of 9 studies involving 328 patients were included. The results of meta-analysis showed that: compared with control group, there were an obvious decrease in the values of LDL (MD=–11.38, 95%CI –17.51 to –5.25, P=0.000 3), TG (MD=–21.14, 95%CI –32.33 to–9.96, P=0.000 2), SBP (MD=–7.96, 95%CI ?11.45 to –4.46, P<0.000 01) and TC, DBP, BMI (P<0.05). However, there were no obvious differences between two groups in HDL. ConclusionsNordic walking can improve the prognosis of patients with cardiovascular diseases, yet the long-term effect is unclear. Due to limited quality and quantity of the included studies, more higher quality studies are required to verify above conclusions.
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 analyze the causal relationship between obstructive sleep apnea (OSA) with its typical symptoms (daytime sleepiness and snoring) and cardiovascular diseases (hypertension, coronary heart disease, myocardial infarction, heart failure) by using Mendelian randomization. MethodsWe used the instrumental variables (IV) in the FINNGen database and the UK Biobank to perform two-sample Mendelian randomization (TSMR) analysis. The results of random-effects inverse variance weighting method (IVW) were the main results. MR-Egger method was used for pleiotropic analysis and sensitivity analysis was performed by the leave-one-out method to verify the reliability of the data. ResultsOSA could lead to hypertension (IVW β=0.043, 95%CI 0.012 to 0.074, P=0.006) and heart failure (IVW β=0.234, 95%CI 0.015 to 0.452, P=0.036). Daytime sleepiness also had a pathogenic effect on heart failure (IVW β=1.139, 95%CI 0.271 to 2.006, P=0.010). There was no causal association between OSA and CHD or MI, snoring and the four CVDs. There was no causal association between daytime sleepiness and hypertension, CHD or MI.ConclusionOSA and daytime sleepiness have pathogenic effects on hypertension and heart failure, with heart failure being the most affected.
目的探討低壓輔助懸吊式腹腔鏡在合并心血管疾病患者行腹腔鏡膽囊切除術（LC）中的應用價值和安全性。 方法回顧性分析2007年1月至2010年10月期間，通渭縣中醫院普外科以及甘肅省人民醫院普外科收治的132例合并心血管疾病的急、慢性膽囊炎或膽囊結石患者的臨床資料。 結果132例患者均進行了低壓輔助懸吊式LC，手術均順利完成，成功率為100%，無中轉開腹，患者術中、術后生命體征正常。 結論低壓輔助懸吊式腹腔鏡技術在合并心血管疾病患者中是安全、可行的。
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 is the leading cause of death worldwide, accounting for 48.0% of all deaths in Europe and 34.3% in the United States. Studies have shown that arterial stiffness takes precedence over vascular structural changes and is therefore considered to be an independent predictor of many cardiovascular diseases. At the same time, the characteristics of Korotkoff signal is related to vascular compliance. The purpose of this study is to explore the feasibility of detecting vascular stiffness based on the characteristics of Korotkoff signal. First, the Korotkoff signals of normal and stiff vessels were collected and preprocessed. Then the scattering features of Korotkoff signal were extracted by wavelet scattering network. Next, the long short-term memory (LSTM) network was established as a classification model to classify the normal and stiff vessels according to the scattering features. Finally, the performance of the classification model was evaluated by some parameters, such as accuracy, sensitivity, and specificity. In this study, 97 cases of Korotkoff signal were collected, including 47 cases from normal vessels and 50 cases from stiff vessels, which were divided into training set and test set according to the ratio of 8 : 2. The accuracy, sensitivity and specificity of the final classification model was 86.4%, 92.3% and 77.8%, respectively. At present, non-invasive screening method for vascular stiffness is very limited. The results of this study show that the characteristics of Korotkoff signal are affected by vascular compliance, and it is feasible to use the characteristics of Korotkoff signal to detect vascular stiffness. This study might be providing a new idea for non-invasive detection of vascular stiffness.