RESUMO
Background: Atherosclerotic coronary heart disease (CHD) stands as a paramount cardiovascular concern and the primary cause of mortality. To underscore the significance of our study, it is crucial to highlight the existing gaps in current diagnostic methods and prognostic assessments of CHD. By addressing these gaps, our research aims to contribute valuable insights and advancements in the understanding and management of this prevalent cardiovascular condition. Objective: The primary objective of this study is to investigate the correlation between carotid ultrasound, the Atherogenic Index of Plasma (AIP), and the severity of CHD. Methods: We enrolled 59 patients diagnosed with coronary heart disease and categorized them into two groups (multi-vessel and single-vessel disease groups) based on disease severity. The study employed carotid ultrasound, which measures Intima-Media Thickness (IMT) and carotid artery stenosis, among other indicators. Additionally, we calculated the AIP. This approach allowed us to thoroughly analyze the correlation between these key indicators and the severity of coronary heart disease lesions. Results: The study included 59 patients, 38 with single-vessel disease and 21 with multi-vessel disease. In the multivessel disease group, we observed significantly elevated levels of AIP, IMT, and carotid stenosis compared to the single-vessel disease group. Specifically, AIP, IMT, and carotid stenosis levels were higher in the multi-vessel group. Furthermore, our analysis revealed a positive correlation between AIP and IMT (r = 0.038, P = .003), while no significant correlation was found between AIP and carotid stenosis. Additionally, there was a moderate correlation between IMT and carotid stenosis. Conclusion: The combined assessment of AIP and carotid ultrasonography emerges as a promising approach for evaluating the severity of CHD. Notably, the multi-vessel disease group exhibited higher AIP levels compared to the single-vessel disease group, along with increased IMT and carotid artery stenosis. Our findings highlight a positive correlation between AIP and IMT, as well as between IMT and the degree of carotid stenosis. These associations underscore the potential of AIP, in conjunction with carotid ultrasonography parameters, as valuable indicators for gauging CHD severity. The clinical implications of these findings warrant further exploration, particularly in their potential integration into existing diagnostic or prognostic models for CHD. This integrated approach may offer enhanced precision in distinguishing between single-vessel and multi-vessel disease, contributing to more informed clinical decision-making.
RESUMO
This paper investigates the methods to detect and classify marmoset vocalizations automatically using a large data set of marmoset vocalizations and deep learning techniques. For vocalization detection, neural networks-based methods, including deep neural network (DNN) and recurrent neural network with long short-term memory units, are designed and compared against a conventional rule-based detection method. For vocalization classification, three different classification algorithms are compared, including a support vector machine (SVM), DNN, and long short-term memory recurrent neural networks (LSTM-RNNs). A 1500-min audio data set containing recordings from four pairs of marmoset twins and manual annotations is employed for experiments. Two test sets are built according to whether the test samples are produced by the marmosets in the training set (test set I) or not (test set II). Experimental results show that the LSTM-RNN-based detection method outperformed others and achieved 0.92% and 1.67% frame error rate on these two test sets. Furthermore, the deep learning models obtained higher classification accuracy than the SVM model, which was 95.60% and 91.67% on the two test sets, respectively.
