Rapid protocols to support Covid-19 clinical diagnosis based on hematological parameters

Gomes, Juliana Carneiro; Barbosa, Valter Augusto de Freitas; Santana, Maíra Araújo de; Lima, Clarisse Lins de; Calado, Raquel Bezerra; Bertoldo, Claudio Roberto; Albuquerque, Jeniffer Emídio de Almeida; Araujo, Ricardo Juarez Escorel de; Moreno, Giselle Machado Magalhães; Soares, Luiz Alberto Lira; Mattos, Luiz Alberto Reis; Souza, Ricardo Emmanuel de; Santos, Wellington Pinheiro dos

doi:10.1101/2021.06.21.21259252

Cited by 4 publications

(1 citation statement)

References 98 publications

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“…The flock's trajectory is then guided by two sources of information: the position of the best particle in relation to the aptitude function and knowledge of the places previously visited by each particle. In this way, at each iteration of the algorithm, the positions and velocities of the particles are adjusted towards the best global and individual position [59][60][61][62][63] . www.nature.com/scientificreports/ Feature selection by PSO was implemented in the database using 30 particles and k-NN as objective function estimator.…”

Section: Feature Selectionmentioning

confidence: 99%

Motor imagery classification using sparse representations: an exploratory study

de Menezes,

Gomes,

de Carvalho Hazin

et al. 2023

Sci Rep

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The non-stationary nature of the EEG signal poses challenges for the classification of motor imagery. sparse representation classification (SRC) appears as an alternative for classification of untrained conditions and, therefore, useful in motor imagery. Empirical mode decomposition (EMD) deals with signals of this nature and appears at the rear of the classification, supporting the generation of features. In this work we evaluate the combination of these methods in a multiclass classification problem, comparing them with a conventional method in order to determine if their performance is regular. For comparison with SRC we use multilayer perceptron (MLP). We also evaluated a hybrid approach for classification of sparse representations with MLP (RSMLP). For comparison with EMD we used filtering by frequency bands. Feature selection methods were used to select the most significant ones, specifically Random Forest and Particle Swarm Optimization. Finally, we used data augmentation to get a more voluminous base. Regarding the first dataset, we observed that the classifiers that use sparse representation have results equivalent to each other, but they outperform the conventional MLP model. SRC and SRMLP achieve an average accuracy of $$75.95\%$$ 75.95 % and $$82.51\%$$ 82.51 % respectively while the MLP is $$72.38\%$$ 72.38 % , representing a gain between $$4.93\%$$ 4.93 % and $$14\%$$ 14 % . The use of EMD in relation to other feature processing techniques is not superior. However, EMD does not influence negatively, there is an opportunity for improvement. Finally, the use of data augmentation proved to be important to obtain relevant results. In the second dataset, we did not observe the same results. Models based on sparse representation (SRC, SRMLP, etc.) have on average a performance close to other conventional models, but without surpassing them. The best sparse models achieve an average accuracy of $$95.43\%$$ 95.43 % among the subjects in the base, while other model reach $$98.33\%$$ 98.33 % . The improvement of self-adaptive mechanisms that respond efficiently to the user’s context is a good way to achieve improvements in motor imagery applications. However, other scenarios should be investigated, since the advantage of these methods was not proven in all datasets studied. There is still room for improvement, such as optimizing the dictionary of sparse representation in the context of motor imagery. Investing efforts in synthetically increasing the training base has also proved important to reduce the costs of this group of applications.

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