Motor imagery (MI) is a domineering paradigm in brain–computer interface (BCI) composition, personifying the imaginary limb motion into digital commandments for neural rehabilitation and automation exertions, while many researchers fathomed myriad solutions for asymmetric MI EEG signals classification, the existence of a robust, non-complex, and subject-invariant system is far-reaching. Thereupon, we put forward an MI EEG segregation pipeline in the deep-learning domain in an effort to curtail the existing limitations. Our method amalgamates multiscale principal component analysis (MSPCA), a novel empirical Fourier decomposition (EFD) signal resolution method with Hilbert transform (HT), followed by four pre-trained convolutional neural networks for automatic feature estimation and segregation. The conceived architecture is validated upon three binary class datasets: IVa, IVb from BCI Competition III, GigaDB from the GigaScience repository, and one tertiary class dataset V from BCI competition III. The average 10-fold outcomes capitulate 98.63%, 96.33%, and 89.96%, the highest classification accuracy for the aforesaid datasets accordingly using the AlexNet CNN model in a subject-dependent context, while in subject-independent cases, the highest success score was 97.69%, outperforming the contemporary studies by a fair margin. Further experiments such as the resolution scale of EFD, comparison with other signal decomposition (SD) methods, deep feature extraction, and classification with machine learning methods also accredits the supremacy of our proposed EEG signal processing pipeline. The overall findings imply that pre-trained models are reliable in identifying EEG signals due to their capacity to maintain the time-frequency structure of EEG signals, non-complex architecture, and their potential for robust classification performance.
Objective To assess the association between the Mannose-binding lectin (MBL) gene polymorphism and the susceptibility to sepsis using a meta-analysis. Methods The publications were searched on PubMed, Embase, and Web of Science databases up to December 1, 2019 for relevant literature. Results A total of 32 studies (21 adult and 11 pediatric studies) were selected for analysis. Overall, in the three models of MBL +54 A/B gene polymorphisms, namely the dominant model BB + AB vs. AA ( p = 0.03), the recessive model BB vs. AB + AA ( p < 0.00001), and the allele model B vs. A ( p = 0.04), MBL +54 A/B was significantly related to the risk of sepsis. In the adult group, the MBL A/O gene polymorphism was associated with the risk of sepsis in the dominant model AO + OO vs. AA ( p = 0.006) as well as in the allele model O vs. A ( p = 0.04). The MBL +54 A/B gene polymorphism was significantly related to the risk of sepsis in the recessive model and, therefore, may increase the risk of sepsis. In the pediatric group, no polymorphic loci were significantly associated with sepsis in any of the three models. The results of the publication bias test demonstrated no publication bias in an unadjusted estimate of the relationship between MBL A/O and −211Y/X gene polymorphism and sepsis. Conclusions The polymorphisms of MBL that are related to the occurrence of sepsis are primarily A/O and +54 A/B, while −221Y/X and −550H/L have no clear relationship with the susceptibility of sepsis in various age groups or different models.
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