Examining the U-shaped relationship of sleep duration and systolic blood pressure with risk of cardiovascular events using a novel recursive gradient scanning model.

Background: Observational studies have suggested U-shaped relationships between sleep duration and systolic blood pressure (SBP) with risks of many cardiovascular diseases (CVDs), but the cut-points that separate high-risk and low-risk groups have not been confirmed. We aimed to examine the U-shaped relationships between sleep duration, SBP, and risks of CVDs and confirm the optimal cut-points for sleep duration and SBP. Methods: A retrospective analysis was conducted on NHANES 2007-2016 data, which included a nationally representative sample of participants. The maximum equal-odds ratio (OR) method was implemented to obtain optimal cut-points for each continuous independent variable. Then, a novel "recursive gradient scanning method" was introduced for discretizing multiple non-monotonic U-shaped independent variables. Finally, a multivariable logistic regression model was constructed to predict critical risk factors associated with CVDs after adjusting for potential confounders. Results: A total of 26,691 participants (48.66% were male) were eligible for the current study with an average age of 49.43 ± 17.69 years. After adjusting for covariates, compared with an intermediate range of sleep duration (6.5-8.0 h per day) and SBP (95-120 mmHg), upper or lower values were associated with a higher risk of CVDs [adjusted OR (95% confidence interval) was 1.20 (1.04-1.40) for sleep duration and 1.17 (1.01-1.36) for SBP]. Conclusions: This study indicates U-shaped relationships between SBP, sleep duration, and risks of CVDs. Both short and long duration of sleep/higher and lower BP are predictors of cardiovascular outcomes. Estimated total sleep duration of 6.5-8.0 h per day/SBP of 95-120 mmHg is associated with lower risk of CVDs.

Differential diagnosis between dilated cardiomyopathy and ischemic cardiomyopathy based on variational mode decomposition and high order spectra analysis.

The clinical manifestations of ischemic cardiomyopathy (ICM) bear resemblance to dilated cardiomyopathy (DCM). The definitive diagnosis of DCM necessitates the identification of invasive, costly, and contraindicated coronary angiography. Many diagnostic studies of cardiovascular disease have tried modal decomposition based on electrocardiogram (ECG) signals. However, these studies ignored the connection between modes and other fields, thus limiting the interpretability of modes to ECG signals and the classification performance of models. This study proposes a classification algorithm based on variational mode decomposition (VMD) and high order spectra, which decomposes the preprocessed ECG signal and extracts its first five modes obtained through VMD. After that, these modes are estimated for their corresponding bispectrums, and the feature vector is composed of fifteen features including bispectral, frequency, and nonlinear features based on this. Finally, a dataset containing 75 subjects (38 DCM, 37 ICM) is classified and compared using random forest (RF), decision tree, support vector machine, and K-nearest neighbor. The results show that, in comparison to previous approaches, the technique proposed provides a better categorization for DCM and ICM of ECG signals, which delivers 98.21% classification accuracy, 98.22% sensitivity, and 98.19% specificity. And mode 3 always has the best performance among single mode. The proposed computerized framework significantly improves automatic diagnostic performance, which can help relieve the working pressure on doctors, possible economic burden and health threaten.