Whether elevated beat-to-beat blood pressure variability (BPV) has an influence on vascular elasticity is confounded and poorly understood. This study hypothesized that the increased BPV could have an adverse effect on the vascular elasticity, as estimated by total arterial compliance (TAC), independent of blood pressure (BP) values. Beat-to-beat BP and TAC were measured in 81 hypertensive patients (experimental population) and in 80 normal adults (control population). Beat-to-beat BPV was assessed by standard deviation (SD), average real variability (ARV), residual standard deviation (RSD) and variation independent of mean (VIM). In experimental population, systolic BPV (SBPV) showed a significant correlation with TAC (SD, r = −0.326, p < 0.001; ARV, r = −0.277, p = 0.003; RSD, r = −0.382, p < 0.001; VIM, r = −0.274, p = 0.003); similarly, SD, RSD and VIM of diastolic BP (DBP) also showed explicit correlation with TAC (r = −0.255, p = 0.006; r = −0.289, p = 0.002; r = −0.219, p = 0.019; respectively). However, in the control population, neither SBPV nor diastolic BPV (DBPV) showed a significant correlation with TAC. Furthermore, in the experimental population, VIM of systolic BP (SBP) was also a determinant of TAC (β = −0.100, p = 0.040) independent of average SBP, DBP, age and body mass index. In conclusion, these data imply that beat-to-beat BPV, especially SBPV, shows an independent correlation with vascular elasticity in hypertensive population.
Arrhythmias reflect electrical abnormalities of the heart, and they can lead to severe harm to the heart. An electrocardiogram (ECG) is a useful tool to manifest arrhythmias. In this paper, we present an automatic system using a convolutional neural network and active learning to classify ECG signals. To improve the model performance, breaking-ties (BT) and modified BT algorithms are utilized in the active learning. We classify ECG signals in five heartbeat types, i.e., normal (N), ventricular (V), supraventricular (S), fusion of normal and ventricular (F), and unknown heartbeats (Q), using the Association for the Advancement of Medical Instrumentation standard. Our experiments are performed on the MIT-BIH arrhythmia database. To further verify the generalization capability of the system, the ECG data that acquired from our wearable device are also used to conduct in the experiments. Compared with most of the stateof-the-art methods, the obtained results demonstrate that the presented method promotes the classification performance remarkably. INDEX TERMS Electrocardiogram, convolutional neural networks, active learning.
Coronary arterial stenoses, particularly serial stenoses in a single branch, are responsible for complex hemodynamic properties of the coronary arterial trees, and the uncertain prognosis of invasive intervention. Critical information of the blood flow redistribution in the stenotic arterial segments is required for the adequate treatment planning. Therefore, in this study, an image based non-invasive functional assessment is performed to investigate the hemodynamic significances of serial stenoses. Twenty patient-specific coronary arterial trees with different combinations of stenoses were reconstructed from the computer tomography angiography for the evaluation of the hemodynamics. Our results showed that the computed FFR based on CTA images (FFRCT) pullback curves with wall shear stress (WSS) distribution could provide more effectively examine the physiological significance of the locations of the segmental narrowing and the curvature of the coronary arterial segments. The paper thus provides the diagnostic efficacy of FFRCT pullback curve for noninvasive quantification of the hemodynamics of stenotic coronary arteries with serial lesions, compared to the gold standard invasive FFR, to provide a reliable physiological assessment of significant amount of coronary artery stenosis. Further, we were also able to demonstrate the potential of carrying out virtual revascularization, to enable more precise PCI procedures and improve their outcomes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12938-017-0413-0) contains supplementary material, which is available to authorized users.
Cardiovascular disease has been the major cause of death worldwide. Although the initiation and progression mechanism of the atherosclerosis are similar, the stenotic characteristics and the corresponding medical decisions are different between individuals. In the present study, we performed anatomic and hemodynamic analysis on 8 left coronary arterial trees with 10 identified stenoses. A novel boundary condition method had been implemented for fast computational fluid dynamics simulations and patient-specific three-dimensional printed models had been built for visualizations. Our results suggested that the multiple spatial characteristics (curvature of the culprit vessel multiplied by an angle of the culprit’s vessel to the upstream parent branch) could be an index of hemodynamics significance (r = −0.673, P-value = 0.033). and reduction of the maximum velocity from stenosis to downstream was found correlated to the FFRCT (r = 0.480, p = 0.160). In addition, 3D printed models could provide accurate replicas of the patient-specific left coronary arterial trees compare to virtual 3D models (r = 0.987, P-value < 0.001). Therefore, the visualization of the 3D printed models could help understand the spatial distribution of the stenoses and the hand-held experience could potentially benefit the educating and preparing of medical strategies.
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