Lightweight Skip Connections With Efficient Feature Stacking for Respiratory Sound Classification

As the number of deaths from respiratory diseases due to COVID-19 and infectious diseases increases, early diagnosis is necessary. In general, the diagnosis of diseases is based on imaging devices (e.g., computed tomography and magnetic resonance imaging) as well as the patient's underlying disease information. However, these examinations are time-consuming, incur considerable costs, and in a situation like the ongoing pandemic, face-to-face examinations are difficult to conduct. Therefore, we propose a lung disease classification model based on deep learning using non-contact auscultation. In this study, two respiratory specialists collected normal respiratory sounds and five types of abnormal sounds associated with lung disease, including those associated with four lung lesions in the left and right anterior chest and left and right posterior chest. For preprocessing and feature extraction, the noise was removed using three pass filters (low, band, and high), and respiratory sound features were extracted using the Log-Mel Spectrogram-Mel Frequency Cepstral Coefficient followed by feature stacking. Then, we propose a lung disease classification model of dense lightweight convolutional neural network-bidirectional gated recurrent unit skip connections using depthwise separable convolution based on the extracted respiratory sound information. The performance of the classification model was compared with both the baseline and the lightweight models. The results indicate that the proposed model achieves high performance and has an accuracy of 92.3%, sensitivity of 92.1%, specificity of 98.5%, and f1-score of 91.9%. Using the proposed model, we aim to contribute to the early detection of diseases during the COVID-19 pandemic.

Ensemble Convolutional Neural Networks With Support Vector Machine for Epilepsy Classification Based on Multi-Sequence of Magnetic Resonance Images

Classification of brain abnormalities as a pathological cue of epilepsy based on magnetic resonance (MR) images is essential for diagnosis. There are some types of brain structural abnormalities as a pathological cue of epilepsy. To identify it, a neurologist can involve some sequence of MR images at a time. Existing algorithms for abnormalities classification usually involve only one or two sequences of MR images. In this paper, we proposed ensemble convolutional neural networks with a support vector machine (SVM) scheme to classify brain abnormalities (epilepsy) vs. non-epilepsy based on the axial multi-sequence of MR images. The convolutional neural network (CNN) models on the proposed method are base-learner models with different architectures and have low parameters. The performance improvement on the proposed method is made by combining the output of the base-learner models and the combination of predictions from these models. The combination of predictions uses majority voting, weighted majority voting, and weighted average. Henceforth, the combined output becomes input in the meta-learning process with SVM for the final classification. The dataset for evaluation is the axial multi-sequences of MR images that include abnormal brain structures causing epilepsy and non-epilepsy with various subjects' histories. The experimental results show the proposed method can obtain an accuracy average and F-1-score of 86.37% and 90.75%, respectively, and an improvement of accuracy of 6.7%-18.19% against the CNN models on the base-learner and 2.54%-2.65% against the combination of predictions. With these results, the proposed architecture also provides better performance compared to the two existing CNN architectures.

User-Centered Virtual Reality Environments to Support the Educational Needs of Children With ADHD in the COVID-19 Pandemic

The use of technology to face the challenges in daily life is something that is increasingly needed and in complicated times like the one we live in today where there is a health contingency (COVID-19) that prevents people from exposing themselves to each other and restricts physical contact, virtual reality can be an alternative that allows the transmission of knowledge in an immersive and interactive way in various fields. This work proposes the use of virtual reality environments as an alternative to support the learning process in children with special educational needs such as Attention Deficit and Hyperactivity Disorder (ADHD) and other associated disorders that occur in basic education. These proposed virtual reality environments are designed under a user-centered approach and their contents are in accordance with expert therapeutic guidelines. As a result of this proposal, a case study is presented in which the user experience is evaluated through the use of an interactive environment to support the special educational needs of elementary school children attending an educational institution in Mexico.

Modeling Early Phases of COVID-19 Pandemic in Northern Italy and Its Implication for Outbreak Diffusion.

The COVID-19 pandemic has sparked an intense debate about the hidden factors underlying the dynamics of the outbreak. Several computational models have been proposed to inform effective social and healthcare strategies. Crucially, the predictive validity of these models often depends upon incorporating behavioral and social responses to infection. Among these tools, the analytic framework known as "dynamic causal modeling" (DCM) has been applied to the COVID-19 pandemic, shedding new light on the factors underlying the dynamics of the outbreak. We have applied DCM to data from northern Italian regions, the first areas in Europe to contend with the outbreak, and analyzed the predictive validity of the model and also its suitability in highlighting the hidden factors governing the pandemic diffusion. By taking into account data from the beginning of the pandemic, the model could faithfully predict the dynamics of outbreak diffusion varying from region to region. The DCM appears to be a reliable tool to investigate the mechanisms governing the spread of the SARS-CoV-2 to identify the containment and control strategies that could efficiently be used to counteract further waves of infection.