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In this study, a thorough analysis of the proposed approach in the context of emotion classification using both single-modal (A-13sbj) and multi-modal (B-12sbj) sets from the YAAD dataset was conducted. This dataset encompassed 25 subjects exposed to audiovisual stimuli designed to induce seven distinct emotional states. Electrocardiogram (ECG) and galvanic skin response (GSR) biosignals were collected and classified using two deep learning models, BEC-1D and ELINA, along with two different preprocessing techniques, a classical fourier-based filtering and an Empirical Mode Decomposition (EMD) approach. For the single-modal set, this proposal achieved an accuracy of 84.43±30.03, precision of 85.16±28.91, and F1-score of 84.06±29.97. Moreover, in the extended configuration the model maintained strong performance, yielding scores of 80.95±22.55, 82.44±24.34, and 79.91±24.55, respectively. Notably, for the multi-modal set (B-12sbj), the best results were obtained with EMD preprocessing and the ELINA model. This proposal achieved an improved accuracy, precision, and F1-score scores of 98.02±3.78, 98.31±3.31, and 97.98±3.83, respectively, demonstrating the effectiveness of this approach in discerning emotional states from biosignals. [ABSTRACT FROM AUTHOR]

Feature selection is a common solution to microarray analysis. Previous approaches either select features based on classical statistical tests that can be tuned up with a classifier, or using regularization penalties incorporated in the cost function. Here we propose to use a feature ranking and weighting scheme instead, which combines statistical techniques with a weighted k -NN classifier using a modified forward selection procedure. We demonstrate that classification accuracy of our proposal outperforms existing methods on a range of public microarray gene expression datasets. The proposed method is also compared to state-of-the-art feature selection algorithms by means of the Friedman test. Although a bunch of feature selection techniques has been used for genomic data, the experimental results show the classification superiority of our method on most of the present gene expression datasets. [ABSTRACT FROM AUTHOR]

Background and Objective: The treatment and care of patients with chronic diseases depends directly on the evolution of biomedical parameters. It is important to have a monitoring health care system that provides biomedical data at any time and place. Here, a multi-sensing health care monitoring system with a built-in non-invasive blood glucose level estimation method is presented.Methods: Six biomedical parameters were obtained from 15 participants. Glucose levels were obtained using a computer vision approach. A standard glucose laboratory test was taken as a baseline, and a commercial glucometer as a secondary reference. The remaining parameters were also contrasted with a commercial vital signs monitor.Results: In comparison to standard test, our proposal reported a better performance (RMSE of 9.811) than obtained with the commercial glucometer; the Mann-Whitney test found no significant differences. The remaining biomedical parameters exhibit similar results to the commercial vital signs monitor as validated by a cardiologist.Conclusion: The results suggest the proposed approach could be considered highly competitive regarding standard tests and validated with commercial health care monitoring systems. [ABSTRACT FROM AUTHOR]