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Article ; Online: Editorial: Internet of Medical Things and computational intelligence in healthcare 4.0.

Dash, Sujata / Pani, Subhendu Kumar / Dos Santos, Wellington Pinheiro

Frontiers in big data

2024 Volume 7, Page(s) 1368581

Language	English
Publishing date	2024-02-21
Publishing country	Switzerland
Document type	Editorial
ISSN	2624-909X
ISSN (online)	2624-909X
DOI	10.3389/fdata.2024.1368581
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Article: Editorial: Machine learning and applied neuroscience.

Dos Santos, Wellington Pinheiro / Conti, Vincenzo / Gambino, Orazio / Naik, Ganesh R

Frontiers in neurorobotics

2023 Volume 17, Page(s) 1191045

Language	English
Publishing date	2023-04-06
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2453002-5
ISSN	1662-5218
ISSN	1662-5218
DOI	10.3389/fnbot.2023.1191045
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Article ; Online: Motor imagery classification using sparse representations: an exploratory study.

de Menezes, José Antonio Alves / Gomes, Juliana Carneiro / de Carvalho Hazin, Vitor / Dantas, Júlio César Sousa / Rodrigues, Marcelo Cairrão Araújo / Dos Santos, Wellington Pinheiro

Scientific reports

2023 Volume 13, Issue 1, Page(s) 15585

Abstract: 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. ...

Abstract	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 [Formula: see text] and [Formula: see text] respectively while the MLP is [Formula: see text], representing a gain between [Formula: see text] and [Formula: see text]. 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 [Formula: see text] among the subjects in the base, while other model reach [Formula: see text]. 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.
Language	English
Publishing date	2023-09-20
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-42790-y
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Article ; Online: COVID-19's influence on cardiac function: a machine learning perspective on ECG analysis.

Gomes, Juliana Carneiro / de Santana, Maíra Araújo / Masood, Aras Ismael / de Lima, Clarisse Lins / Dos Santos, Wellington Pinheiro

Medical & biological engineering & computing

2023 Volume 61, Issue 5, Page(s) 1057–1081

Abstract: In December 2019, the spread of the SARS-CoV-2 virus to the world gave rise to probably the biggest public health problem in the world: the COVID-19 pandemic. Initially seen only as a disease of the respiratory system, COVID-19 is actually a blood ... ...

Abstract	In December 2019, the spread of the SARS-CoV-2 virus to the world gave rise to probably the biggest public health problem in the world: the COVID-19 pandemic. Initially seen only as a disease of the respiratory system, COVID-19 is actually a blood disease with effects on the respiratory tract. Considering its influence on hematological parameters, how does COVID-19 affect cardiac function? Is it possible to support the clinical diagnosis of COVID-19 from the automatic analysis of electrocardiography? In this work, we sought to investigate how COVID-19 affects cardiac function using a machine learning approach to analyze electrocardiography (ECG) signals. We used a public database of ECG signals expressed as photographs of printed signals, obtained in the context of emergency care. This database has signals associated with abnormal heartbeat, myocardial infarction, history of myocardial infarction, COVID-19, and healthy heartbeat. We propose a system to support the diagnosis of COVID-19 based on hybrid deep architectures composed of pre-trained convolutional neural networks for feature extraction and Random Forests for classification. We investigated the LeNet, ResNet, and VGG16 networks. The best results were obtained with the VGG16 and Random Forest network with 100 trees, with attribute selection using particle swarm optimization. The instance size has been reduced from 4096 to 773 attributes. In the validation step, we obtained an accuracy of 94%, kappa index of 0.91, and sensitivity, specificity, and area under the ROC curve of 100%. This work showed that the influence of COVID-19 on cardiac function is quite considerable: COVID-19 did not present confusion with any heart disease, nor with signs of healthy individuals. It is also possible to build a solution to support the clinical diagnosis of COVID-19 in the context of emergency care from a non-invasive and technologically scalable solution, based on hybrid deep learning architectures.
MeSH term(s)	Humans ; COVID-19/diagnosis ; SARS-CoV-2 ; Pandemics ; Machine Learning ; Electrocardiography ; Myocardial Infarction/diagnosis
Language	English
Publishing date	2023-01-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	282327-5
ISSN	1741-0444 ; 0025-696X ; 0140-0118
ISSN (online)	1741-0444
ISSN	0025-696X ; 0140-0118
DOI	10.1007/s11517-023-02773-7
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Article ; Online: Image Segmentation Techniques for Healthcare Systems.

Gambino, Orazio / Conti, Vincenzo / Galdino, Sergio / Valenti, Cesare Fabio / Dos Santos, Wellington Pinheiro

Journal of healthcare engineering

2019 Volume 2019, Page(s) 2723419

MeSH term(s)	Delivery of Health Care ; Humans ; Image Interpretation, Computer-Assisted ; Image Processing, Computer-Assisted
Language	English
Publishing date	2019-04-02
Publishing country	England
Document type	Editorial
ZDB-ID	2545054-2
ISSN	2040-2309 ; 2040-2295
ISSN (online)	2040-2309
ISSN	2040-2295
DOI	10.1155/2019/2723419
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Article ; Online: MEWAR: Development of a Cross-Platform Mobile Application and Web Dashboard System for Real-Time Mosquito Surveillance in Northeast Brazil.

Aldosery, Aisha / Musah, Anwar / Birjovanu, Georgiana / Moreno, Giselle / Boscor, Andrei / Dutra, Livia / Santos, George / Nunes, Vania / Oliveira, Rossandra / Ambrizzi, Tercio / Massoni, Tiago / Dos Santos, Wellington Pinheiro / Kostkova, Patty

Frontiers in public health

2021 Volume 9, Page(s) 754072

Abstract: Mosquito surveillance is a crucial process for understanding the population dynamics of mosquitoes, as well as implementing interventional programs for controlling and preventing the spread of mosquito-borne diseases. Environmental surveillance agents ... ...

Abstract	Mosquito surveillance is a crucial process for understanding the population dynamics of mosquitoes, as well as implementing interventional programs for controlling and preventing the spread of mosquito-borne diseases. Environmental surveillance agents who performing routine entomological surveys at properties in areas where mosquito-borne diseases are endemic play a critical role in vector surveillance by searching and destroying mosquito hotspots as well as collate information on locations with increased infestation. Currently, the process of recording information on paper-based forms is time-consuming and painstaking due to manual effort. The introduction of mobile surveillance applications will therefore improve the process of data collection, timely reporting, and field worker performance. Digital-based surveillance is critical in reporting real-time data; indeed, the real-time capture of data with phones could be used for predictive analytical models to predict mosquito population dynamics, enabling early warning detection of hotspots and thus alerting fieldworker agents into immediate action. This paper describes the development of a cross-platform digital system for improving mosquito surveillance in Brazil. It comprises of two components: a dashboard for managers and a mobile application for health agents. The former enables managers to assign properties to health workers who then survey them for mosquitoes and to monitor the progress of inspection visits in real-time. The latter, which is primarily designed as a data collection tool, enables the environmental surveillance agents to act on their assigned tasks of recording the details of the properties at inspections by filling out digital forms built into the mobile application, as well as details relating to mosquito infestation. The system presented in this paper was co-developed with significant input with environmental agents in two Brazilian cities where it is currently being piloted.
MeSH term(s)	Animals ; Brazil ; Culicidae ; Entomology ; Humans ; Mobile Applications ; Mosquito Vectors
Language	English
Publishing date	2021-10-27
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2711781-9
ISSN	2296-2565 ; 2296-2565
ISSN (online)	2296-2565
ISSN	2296-2565
DOI	10.3389/fpubh.2021.754072
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Article ; Online: Detection and classification of masses in mammographic images in a multi-kernel approach.

de Lima, Sidney M L / da Silva-Filho, Abel G / Dos Santos, Wellington Pinheiro

Computer methods and programs in biomedicine

2016 Volume 134, Page(s) 11–29

Abstract: Background and objective: According to the World Health Organization, breast cancer is the main cause of cancer death among adult women in the world. Although breast cancer occurs indiscriminately in countries with several degrees of social and economic ...

Abstract	Background and objective: According to the World Health Organization, breast cancer is the main cause of cancer death among adult women in the world. Although breast cancer occurs indiscriminately in countries with several degrees of social and economic development, among developing and underdevelopment countries mortality rates are still high due to low availability of early detection technologies. From the clinical point of view, mammography is still the most effective diagnostic technology, given the wide diffusion of the use and interpretation of these images. Methods: Herein this work we propose a method to detect and classify mammographic lesions using the regions of interest of images. Our proposal consists in decomposing each image using multi-resolution wavelets. Zernike moments are extracted from each wavelet component. Using this approach, we can combine both texture and shape features, which can be applied both to the detection and classification of mammary lesions. We used 355 images of fatty breast tissue of IRMA database, with 233 normal instances (no lesion), 72 benign, and 83 malignant cases. Results: Classification was performed by using SVM and ELM networks with modified kernels in order to optimize accuracy rates, reaching 94.11%. Considering both accuracy rates and training times, we defined the ration between average percentage accuracy and average training time in a reverse order. Our proposal was 50 times higher than the ratio obtained using state-of-the-art approaches. Conclusions: As our proposed model can combine high accuracy rate with low learning time, whenever a new data is received, our work will be able to save a lot of time, hours, in learning process in relation to the best method of the state-of-the-art.
MeSH term(s)	Breast Neoplasms/classification ; Breast Neoplasms/diagnostic imaging ; Female ; Humans ; Mammography ; Neural Networks (Computer)
Language	English
Publishing date	2016-10
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	632564-6
ISSN	1872-7565 ; 0169-2607
ISSN (online)	1872-7565
ISSN	0169-2607
DOI	10.1016/j.cmpb.2016.04.029
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Article ; Online: Covid-19 rapid test by combining a Random Forest-based web system and blood tests.

Barbosa, Valter Augusto de Freitas / Gomes, Juliana Carneiro / de Santana, Maíra Araújo / de Lima, Clarisse Lins / Calado, Raquel Bezerra / Bertoldo Júnior, Cláudio Roberto / Albuquerque, Jeniffer Emidio de Almeida / de Souza, Rodrigo Gomes / de Araújo, Ricardo Juarez Escorel / Mattos Júnior, Luiz Alberto Reis / de Souza, Ricardo Emmanuel / Dos Santos, Wellington Pinheiro

Journal of biomolecular structure & dynamics

2021 Volume 40, Issue 22, Page(s) 11948–11967

Abstract: The disease caused by the new type of coronavirus, Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-CoV-2 has already ... ...

Abstract	The disease caused by the new type of coronavirus, Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-CoV-2 has already caused over 2 million deaths to date. In this work, we propose a web solution, called Heg.IA, to optimize the diagnosis of Covid-19 through the use of artificial intelligence. Our system aims to support decision-making regarding to diagnosis of Covid-19 and to the indication of hospitalization on regular ward, semi-ICU or ICU based on decision a Random Forest architecture with 90 trees. The main idea is that healthcare professionals can insert 41 hematological parameters from common blood tests and arterial gasometry into the system. Then, Heg.IA will provide a diagnostic report. The system reached good results for both Covid-19 diagnosis and to recommend hospitalization. For the first scenario we found average results of accuracy of 92.891%±0.851, kappa index of 0.858 ± 0.017, sensitivity of 0.936 ± 0.011, precision of 0.923 ± 0.011, specificity of 0.921 ± 0.012 and area under ROC of 0.984 ± 0.003. As for the indication of hospitalization, we achieved excellent performance of accuracies above 99% and more than 0.99 for the other metrics in all situations. By using a computationally simple method, based on the classical decision trees, we were able to achieve high diagnosis performance. Heg.IA system may be a way to overcome the testing unavailability in the context of Covid-19.Communicated by Ramaswamy H. Sarma.
MeSH term(s)	Humans ; COVID-19/diagnosis ; SARS-CoV-2 ; COVID-19 Testing ; Random Forest ; Artificial Intelligence ; Hematologic Tests
Language	English
Publishing date	2021-08-31
Publishing country	England
Document type	Journal Article
ZDB-ID	49157-3
ISSN	1538-0254 ; 0739-1102
ISSN (online)	1538-0254
ISSN	0739-1102
DOI	10.1080/07391102.2021.1966509
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Article ; Online: Covid-19 diagnosis by combining RT-PCR and pseudo-convolutional machines to characterize virus sequences.

Gomes, Juliana Carneiro / Masood, Aras Ismael / Silva, Leandro Honorato de S / da Cruz Ferreira, Janderson Romário B / Freire Júnior, Agostinho Antônio / Rocha, Allana Laís Dos Santos / de Oliveira, Letícia Castro Portela / da Silva, Nathália Regina Cauás / Fernandes, Bruno José Torres / Dos Santos, Wellington Pinheiro

Scientific reports

2021 Volume 11, Issue 1, Page(s) 11545

Abstract: The Covid-19 pandemic, a disease transmitted by the SARS-CoV-2 virus, has already caused the infection of more than 120 million people, of which 70 million have been recovered, while 3 million people have died. The high speed of infection has led to the ... ...

Abstract	The Covid-19 pandemic, a disease transmitted by the SARS-CoV-2 virus, has already caused the infection of more than 120 million people, of which 70 million have been recovered, while 3 million people have died. The high speed of infection has led to the rapid depletion of public health resources in most countries. RT-PCR is Covid-19's reference diagnostic method. In this work we propose a new technique for representing DNA sequences: they are divided into smaller sequences with overlap in a pseudo-convolutional approach and represented by co-occurrence matrices. This technique eliminates multiple sequence alignment. Through the proposed method, it is possible to identify virus sequences from a large database: 347,363 virus DNA sequences from 24 virus families and SARS-CoV-2. When comparing SARS-CoV-2 with virus families with similar symptoms, we obtained [Formula: see text] for sensitivity and [Formula: see text] for specificity with MLP classifier and 30% overlap. When SARS-CoV-2 is compared to other coronaviruses and healthy human DNA sequences, we obtained [Formula: see text] for sensitivity and [Formula: see text] for specificity with MLP and 50% overlap. Therefore, the molecular diagnosis of Covid-19 can be optimized by combining RT-PCR and our pseudo-convolutional method to identify DNA sequences for SARS-CoV-2 with greater specificity and sensitivity.
MeSH term(s)	COVID-19 Nucleic Acid Testing/methods ; Computational Biology/methods ; DNA, Viral ; Humans ; Machine Learning ; Reverse Transcriptase Polymerase Chain Reaction/methods ; SARS-CoV-2/genetics ; Sensitivity and Specificity ; Support Vector Machine ; Viruses/genetics
Chemical Substances	DNA, Viral
Language	English
Publishing date	2021-06-02
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-021-90766-7
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Article ; Online: Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review.

de Lima, Clarisse Lins / da Silva, Ana Clara Gomes / Moreno, Giselle Machado Magalhães / Cordeiro da Silva, Cecilia / Musah, Anwar / Aldosery, Aisha / Dutra, Livia / Ambrizzi, Tercio / Borges, Iuri V G / Tunali, Merve / Basibuyuk, Selma / Yenigün, Orhan / Massoni, Tiago Lima / Browning, Ella / Jones, Kate / Campos, Luiza / Kostkova, Patty / da Silva Filho, Abel Guilhermino / Dos Santos, Wellington Pinheiro

Frontiers in public health

2022 Volume 10, Page(s) 900077

Abstract: Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a ...

Abstract	Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models.
MeSH term(s)	Animals ; Arbovirus Infections/epidemiology ; Arbovirus Infections/transmission ; Arbovirus Infections/virology ; Arboviruses/classification ; Arboviruses/pathogenicity ; Arboviruses/physiology ; Arthropod Vectors/classification ; Arthropod Vectors/virology ; Humans ; Machine Learning/standards ; Machine Learning/trends ; Models, Statistical ; Neglected Diseases/epidemiology ; Neglected Diseases/virology ; Public Health/methods ; Public Health/trends
Language	English
Publishing date	2022-06-03
Publishing country	Switzerland
Document type	Systematic Review ; Research Support, Non-U.S. Gov't
ZDB-ID	2711781-9
ISSN	2296-2565 ; 2296-2565
ISSN (online)	2296-2565
ISSN	2296-2565
DOI	10.3389/fpubh.2022.900077
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