BEMD-3DCNN-based method for COVID-19 detection.

The coronavirus outbreak continues to spread around the world and no one knows when it will stop. Therefore, from the first day of the identification of the virus in Wuhan, China, scientists have launched numerous research projects to understand the nature of the virus, how to detect it, and search for the most effective medicine to help and protect patients. Importantly, a rapid diagnostic and detection system is a priority and should be developed to stop COVID-19 from spreading. Medical imaging techniques have been used for this purpose. Current research is focused on exploiting different backbones like VGG, ResNet, DenseNet, or combining them to detect COVID-19. By using these backbones many aspects cannot be analyzed like the spatial and contextual information in the images, although this information can be useful for more robust detection performance. In this paper, we used 3D representation of the data as input for the proposed 3DCNN-based deep learning model. The process includes using the Bi-dimensional Empirical Mode Decomposition (BEMD) technique to decompose the original image into IMFs, and then building a video of these IMF images. The formed video is used as input for the 3DCNN model to classify and detect the COVID-19 virus. The 3DCNN model consists of a 3D VGG-16 backbone followed by a Context-aware attention (CAA) module, and then fully connected layers for classification. Each CAA module takes the feature maps of different blocks of the backbone, which allows learning from different feature maps. In our experiments, we used 6484 X-ray images, of which 1802 were COVID-19 positive cases, 1910 normal cases, and 2772 pneumonia cases. The experiment results showed that our proposed technique achieved the desired results on the selected dataset. Additionally, the use of the 3DCNN model with contextual information processing exploited CAA networks to achieve better performance.

Long-Term Hospital Readmission after ST-Elevation Myocardial Infarction: A 3-Year Follow-up from the SEMI-CI Study.

Background: This study aimed to investigate readmission risk factors after ST-elevation myocardial infarction (STEMI) during a 3-year follow-up. Methods: This study is a secondary analysis of the STEMI Cohort Study (SEMI-CI) in Isfahan, Iran, with 867 patients. A trained nurse gathered the demographic, medical history, laboratory, and clinical data at discharge. Then the patients were followed up annually for 3 years by telephone and invitation for in-person visits with a cardiologist concerning readmission status. Cardiovascular readmission was defined as MI, unstable angina, stent thrombosis, stroke, and heart failure. Adjusted and unadjusted binary logistic regression analyses were applied. Results: Of 773 patients with complete information, 234 patients (30.27%) experienced 3-year readmission. The mean age of the patients was 60.92±12.77 years, and 705 patients (81.3%) were males. The unadjusted results showed that smokers were 21% more likely to be readmitted than nonsmokers (OR, 1.21; P=0.015). Readmitted patients had a 26% lower shock index (OR, 0.26; P=0.047), and ejection fraction had a conservative effect (OR, 0.97; P<0.05). The creatinine level was 68% higher in patients with readmission. An adjusted model based on age and sex showed that the creatinine level (OR, 1.73), the shock index (OR, 0.26), heart failure (OR, 1.78), and ejection fraction (OR, 0.97) were significantly different between the 2 groups. Conclusion: Patients at risk of readmission should be identified and carefully visited by specialists to help improve timely treatment and reduce readmissions. Therefore, it is recommended to pay special attention to factors affecting readmission in the routine visits of STEMI patients.

A different approach to use narrowband super-resolution multiple signal classification algorithm on wideband sources.

There are varieties of wideband direction-of-arrival (DOA) estimation algorithms. Their structure comprises a number of narrowband ones, each performs in one frequency in a given bandwidth, and then different responses should be combined in a proper way to yield true DOAs. Hence, wideband algorithms are always complex and so non-real-time. This paper investigates a method to derive a flat response of narrowband multiple signal classification (MUSIC) [R. O. Schmidt, IEEE Trans. Antennas Propag., 34, 276-280 (1986)] algorithm in the whole frequencies of given band. Therefore, required conditions of applying narrowband algorithm on wideband impinging signals will be given through a concrete analysis. It could be found out that array sensor locations are able to compensate the frequency variations to reach a flat response of DOAs in a specified wideband frequency.