A carregar...
Projeto de investigação
Sem título
Financiador
Autores
Publicações
A cluster-based opposition differential evolution algorithm boosted by a local search for ECG signal classification
Publication . Pourvahab, Mehran; Mousavirad, Seyed Jalaleddin; Felizardo, Virginie; Pombo, Nuno; Zacarias, Henriques; Mohammadigheymasi, Hamzeh; Pais, Sebastião; Jafari, Seyed Nooreddin; Garcia, Nuno M.; NOVALincs; Elsevier BV
Electrocardiogram (ECG) signals, which capturethe heart's electrical activity, are used to diagnose and monitor cardiac problems. The accurate classification of ECG signals, particularly for distinguishing among various types of arrhythmias and myocardial infarctions, is crucial for the early detection and treatment of heart-related diseases. This paper proposes a novel approach based on an improved differential evolution (DE) algorithm for ECG signal classification for enhancing the performance. In the initial stages of our approach, the preprocessing step is followed by the extraction of several significant features from the ECG signals. These extracted features are then provided as inputs to an enhanced multi-layer perceptron (MLP). While MLPs are still widely used for ECG signal classification, using gradient-based training methods, the most widely used algorithm for the training process, has significant disadvantages, such as the possibility of being stuck in local optimums. This paper employs an enhanced differential evolution (DE) algorithm for the training process as one of the most effective population-based algorithms. To this end, we improved DE based on a clustering-based strategy, opposition-based learning, and a local search. Clustering-based strategies can act as crossover operators, while the goal of the opposition operator is to improve the exploration of the DE algorithm. The weights and biases found by the improved DE algorithm are then fed into six gradient-based local search algorithms. In other words, the weights found by the DE are employed as an initialization point. Therefore, we introduced six different algorithms for the training process (in terms of different local search algorithms). In an extensive set of experiments, we showed that our proposed training algorithm could provide better results than the conventional training algorithms.
Impact of five basic tastes perception on neurophysiological response
Publication . Pereira, Diana Rico; Pereira, Helena Rico; Silva, Maria Leonor; Pereira, Paula; Ferreira, Hugo Alexandre; CTS - Centro de Tecnologia e Sistemas; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias; Elsevier
The five basic tastes (sweet, salty, umami, sour, and bitter) perception plays a fundamental role in food choices. Nevertheless, how the perception of each basic tastes influence brain activity is still unknown. We investigated the effect of each taste on the brain activity of healthy adults using electroencephalography (EEG). For that, sucrose, sodium chloride, sodium glutamate, citric acid, and caffeine solutions were individually administered to 28 participants (18–25 years old). Self-reporting feedback was assessed using the 3-dimensional Self-Assessment-Manikin (SAM). The power density of the five frequency bands (delta, theta, alpha, beta and gamma) computed from the EEG signals was used to compare the five basic tastes. Significant differences (p < 0.05) were found for (1) beta waves: sweet vs umami, and salty vs umami; and for (2) gamma waves: sweet vs umami, and sweet vs bitter. The findings also indicate that sweet taste stimulated higher brain activity than umami in the gamma but not in the beta waves. Sweet, salty, umami and bitter tastes correlated with SAM responses. This study advances the understanding of brain response to taste stimuli, whilst improving the knowledge of these sensorial cognitive processes.
Recovering Image Quality in Low-Dose Pediatric Renal Scintigraphy Using Deep Learning
Publication . Arsénio, Marta; Vigário, Ricardo; Mota, Ana M.; LIBPhys-UNL; DF – Departamento de Física; MDPI - Multidisciplinary Digital Publishing Institute
The objective of this study is to propose an advanced image enhancement strategy to address the challenge of reducing radiation doses in pediatric renal scintigraphy. Data from a public dynamic renal scintigraphy database were used. Based on noisier images, four denoising neural networks (DnCNN, UDnCNN, DUDnCNN, and AttnGAN) were evaluated. To evaluate the quality of the noise reduction, with minimal detail loss, the kidney signal-to-noise ratio (SNR) and multiscale structural similarity (MS-SSIM) were used. Although all the networks reduced noise, UDnCNN achieved the best balance between SNR and MS-SSIM, leading to the most notable improvements in image quality. In clinical practice, 100% of the acquired data are summed to produce the final image. To simulate the dose reduction, we summed only 50%, simulating a proportional decrease in radiation. The proposed deep-learning approach for image enhancement ensured that half of all the frames acquired may yield results that are comparable to those of the complete dataset, suggesting that it is feasible to reduce patients’ exposure to radiation. This study demonstrates that the neural networks evaluated can markedly improve the renal scintigraphic image quality, facilitating high-quality imaging with lower radiation doses, which will benefit the pediatric population considerably.
Unidades organizacionais
Descrição
Palavras-chave
Contribuidores
Financiadores
Entidade financiadora
Fundação para a Ciência e a Tecnologia
Programa de financiamento
Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) - Financiamento Base
Número da atribuição
UIDB/00645/2020