Browsing by Author "Silva, Gabriel"
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- Classification of sleep quality and aging as a function of brain complexity: a multiband non-linear EEG analysisPublication . Penalba-Sánchez, Lucía; Silva, Gabriel; Crook-Rumsey, Mark; Sumich, Alexander; Rodrigues, Pedro Miguel; Oliveira-Silva, Patrícia; Cifre, IgnacioUnderstanding and classifying brain states as a function of sleep quality and age has important implications for developing lifestyle-based interventions involving sleep hygiene. Current studies use an algorithm that captures non-linear features of brain complexity to differentiate awake electroencephalography (EEG) states, as a function of age and sleep quality. Fifty-eight participants were assessed using the Pittsburgh Sleep Quality Inventory (PSQI) and awake resting state EEG. Groups were formed based on age and sleep quality (younger adults n = 24, mean age = 24.7 years, SD = 3.43, good sleepers n = 11; older adults n = 34, mean age = 72.87; SD = 4.18, good sleepers n = 9). Ten non-linear features were extracted from multiband EEG analysis to feed several classifiers followed by a leave-one-out cross-validation. Brain state complexity accurately predicted (i) age in good sleepers, with 75% mean accuracy (across all channels) for lower frequencies (alpha, theta, and delta) and 95% accuracy at specific channels (temporal, parietal); and (ii) sleep quality in older groups with moderate accuracy (70 and 72%) across sub-bands with some regions showing greater differences. It also differentiated younger good sleepers from older poor sleepers with 85% mean accuracy across all sub-bands, and 92% at specific channels. Lower accuracy levels (<50%) were achieved in predicting sleep quality in younger adults. The algorithm discriminated older vs. younger groups excellently and could be used to explore intragroup differences in older adults to predict sleep intervention efficiency depending on their brain complexity.
- COVID-19 activity screening by a smart-data-driven multi-band voice analysisPublication . Silva, Gabriel; Batista, Patrícia; Rodrigues, Pedro MiguelCOVID-19 is a disease caused by the new coronavirus SARS-COV-2 which can lead to severe respiratory infections. Since its first detection it caused more than six million worldwide deaths. COVID-19 diagnosis non-invasive and low-cost methods with faster and accurate results are still needed for a fast disease control. In this research, 3 different signal analyses have been applied (per broadband, per sub-bands and per broadband & sub-bands) to Cough, Breathing & Speech signals of Coswara dataset to extract non-linear patterns (Energy, Entropies, Correlation Dimension, Detrended Fluctuation Analysis, Lyapunov Exponent & Fractal Dimensions) for feeding a XGBoost classifier to discriminate COVID-19 activity on its different stages. Classification accuracies ranged between 83.33% and 98.46% have been achieved, surpassing the state-of-art methods in some comparisons. It should be empathized the 98.46% of accuracy reached on pair Healthy Controls vs all COVID-19 stages. The results shows that the method may be adequate for COVID-19 diagnosis screening assistance.
- Detecção precoce das doenças de Alzheimer e Parkinson através de parâmetros não-lineares multibanda de sinais EEGPublication . Silva, Gabriel; Alves, Marco; Bispo, Bruno C.; Rodrigues, Pedro M.Este trabalho tem como objetivo a detecção precoce das doenças de Alzheimer e Parkinson através de parâmetros não-lineares multibanda de sinais EEG. Para cada par de grupos de estudo, uma seleção dos parâmetros é realizada através de algoritmo genético. Os parâmetros selecionados são utilizados como entrada para classificadores com validação cruza da leave-one-out. Acurácias de classificação de 100% são obtidas, empelo menos uma sub-banda, para 3 pares de grupos de estudo enquanto 90,60% é alcançado para o par Controle vs Alzhei-mer/Parkinson. A sub-banda delta foi a que, em geral, apresentou maiores diferenças significativas entre os grupos.
- Exploring the relationship between CAIDE dementia risk and EEG signal activity in a healthy populationPublication . Manuel, Alice Rodrigues; Ribeiro, Pedro; Silva, Gabriel; Rodrigues, Pedro Miguel; Nunes, Maria Vânia SilvaBackground: Accounting for dementia risk factors is essential in identifying people who would benefit most from intervention programs. The CAIDE dementia risk score is commonly applied, but its link to brain function remains unclear. This study aims to determine whether the variation in this score is associated with neurophysiological changes and cognitive measures in normative individuals. Methods: The sample comprised 38 participants aged between 54 and 79 (M = 67.05; SD = 6.02). Data were collected using paper-and-pencil tests and electroencephalogram (EEG) recordings in the resting state, channels FP1 and FP2. The EEG signals were analyzed using Power Spectral Density (𝑃𝑆𝐷)-based features. Results: The CAIDE score is positively correlated with the relative power activation of the 𝜃 band and negatively correlated with the MMSE cognitive test score, and MMSE variations align with those found in distributions of EEG-extracted 𝑃𝑆𝐷-based features. Conclusions: The findings suggest that CAIDE scores can identify individuals without noticeable cognitive changes who already exhibit brain activity similar to that seen in people with dementia. They also contribute to the convergent validity between CAIDE and the risk of cognitive decline. This underscores the importance of early monitoring of these factors to reduce the incidence of dementia.
- Validation of psychophysiological measures for caffeine oral films characterization by machine learning approachesPublication . Batista, Patrícia; Rodrigues, Pedro Miguel; Ferreira, Miguel; Moreno, Ana; Silva, Gabriel; Alves, Marco; Pintado, Manuela; Oliveira-Silva, PatríciaBackground: The oral films are a new delivery system that can carry several molecules, such as neuromodulator molecules, including caffeine. These delivery systems have been developed and characterized by pharmacokinetics assays. However, new methodologies, such as psychophysiological measures, can complement their characterization. This study presents a new protocol with psychophysiological parameters to characterize the oral film delivery systems based on a caffeine model. (2) Methods: Thirteen volunteers (61.5% females and 38.5% males) consumed caffeine oral films and placebo oral films (in different moments and without knowing the product). Electrocardiogram (ECG), electrodermal (EDA), and respiratory frequency (RF) data were monitored for 45 min. For the data analysis, the MATLAB environment was used to develop the analysis program. The ECG, EDA, and RF signals were digitally filtered and processed, using a windowing process, for feature extraction and an energy mean value for 5 min segments. Then, the data were computed and presented to the entries of a set of Machine Learning algorithms. Finally, a data statistical analysis was carried out using SPSS. (3) Results: Compared with placebo, caffeine oral films led to a significant increase in power energy in the signal spectrum of heart rate, skin conductance, and respiratory activity. In addition, the ECG time-series power energy activity revealed a better capacity to detect caffeine activity over time than the other physiological modalities. There was no significant change for the female or male gender. (4) Conclusions: The protocol developed, and the psychophysiological methodology used to characterize the delivery system profile were efficient to characterize the drug delivery profile of the caffeine. This is a non-invasive, cheap, and easy method to apply, can be used to determine the neuromodulator drugs delivery profile, and can be implemented in the future.