Browsing by Author "Rodrigues, Pedro Miguel"
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- Algoritmo para o fator de esquecimento do método cepstral de cancelamento de realimentação acústicaPublication . Nogueira, Wellington M. da S.; Yamamura, Cézar F.; Bispo, Bruno C.; Theodoro, Edson; Rodrigues, Pedro MiguelEste trabalho propõe uma melhoria no método de cancelamento de realimentação acústica baseado no cepstro do sinal de erro. Essa melhoria consiste em um algoritmo para transformar o fator de esquecimento, parâmetro que controla o compromisso entre robustez a perturbações de curta duração e capacidade de rastreamento do filtro adaptativo, em variante no tempo. Simulações demonstraram que esse algoritmo faz o método apresentar um melhor compromisso entre velocidade de (re-)convergência e limite de desalinhamento, aumentando de modo geral a margem de estabilidade do sistema de sonorização.
- Uma análise do método cepstral de cancelamento de realimentação acústicaPublication . Amaral, Marcelo Carsten; Rodrigues, Pedro Miguel; Bispo, Bruno CatarinoEsse trabalho analisa o método cepstral de cance- lamento de realimentação acústica. Demonstra-se que o erro do filtro adaptativo é composto pela soma de dois termos. O primeiro é relacionado somente ao caminho de realimentação e tende a zero, convergindo mais rapidamente para valores menores do fator de esquecimento. O segundo é relacionado aos cepstros do sinal de entrada e ao ganho do sistema de sonorização, contendo uma soma ponderada dos cepstros em que as ponderações são potências do fator de esquecimento. Simulações mostraram que, para sinais de fala, a soma ponderada dos cepstros converge, fato preponderante para a convergência do método. A velocidade de convergência, o valor após a convergência e as oscilações ao redor desse valor do segundo termo diminuem com o aumento do fator de esquecimento, comportamentos diretamente refletidos no erro do filtro. Nas primeiras iterações, o primeiro termo tem maior influência. Após algumas iterações, o segundo termo rege o desempenho do método.
- Analysis of acoustic feedback cancellation systems based on direct closed-loop identificationPublication . Bispo, Bruno C.; Yamamura, Cézar; Nogueira, Wellington M. S.; Theodoro, Edson A. R.; Rodrigues, Pedro MiguelThis work presents, using the least squares estimation theory, a theoretical and experimental analysis on the performance of the standard adaptive filtering algorithms when applied to acoustic feedback cancellation. Expressions for the bias and covariance matrix of the acoustic feedback path estimate provided by these algorithms are derived as a function of the signals statistics as well as derivatives of the cost function. It is demonstrated that, in general, the estimate is biased and presents a large covariance because the closed-loop nature of the system makes the cross-correlation between the loudspeaker and system input signals non-zero. Simulations are carried out to exemplify the results using speech signals, a long acoustic feedback path and the recursive least squares algorithm. The results illustrate that these algorithms converge very slowly to a solution that is not the true acoustic feedback path. The relationship between the performance of the adaptive filtering algorithms and the aforementioned cross-correlation is proven by varying the signal-to-noise ratio and the delay introduced by the forward path.
- Cardiovascular diseases diagnosis using an ECG multi-band non-linear machine learning framework analysisPublication . Ribeiro, Pedro; Sá, Joana; Paiva, Daniela; Rodrigues, Pedro MiguelBackground: cardiovascular diseases (CVDs), which encompass heart and blood vessel issues, stand as the leading cause of global mortality for many people. Methods: the present study intends to perform discrimination between seven well-known CVDs (bundle branch block, cardiomyopathy, myocarditis, myocardial hypertrophy, myocardial infarction, valvular heart disease, and dysrhythmia) and one healthy control group, respectively, by feeding a set of machine learning (ML) models with 10 non-linear features extracted every 1 s from electrocardiography (ECG) lead signals of a well-known ECG database (PTB diagnostic ECG database) using multi-band analysis performed by discrete wavelet transform (DWT). The ML models were trained and tested using a leave-one-out cross-validation approach, assessing the individual and combined capabilities of features, per each lead or combined, to distinguish between pairs of study groups and for conducting a comprehensive all vs. all analysis. Results: the 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 discrimination results ranged between 73% and 100%, the 𝑅𝑒𝑐𝑎𝑙𝑙 between 68% and 100%, and the 𝐴𝑈𝐶 between 0.42 and 1. Conclusions: the results suggest that our method is a good tool for distinguishing CVDs, offering significant advantages over other studies that used the same dataset, including a multi-class comparison group (all vs. all), a wider range of binary comparisons, and the use of classical non-linear analysis under ECG multi-band analysis performed by DWT.
- 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.
- COVID-19 detection by means of ECG, Voice, and X-ray computerized systems: a reviewPublication . Ribeiro, Pedro; Marques, João Alexandre Lobo; Rodrigues, Pedro MiguelSince the beginning of 2020, Coronavirus Disease 19 (COVID-19) has attracted the attention of the World Health Organization (WHO). This paper looks into the infection mechanism, patient symptoms, and laboratory diagnosis, followed by an extensive assessment of different technologies and computerized models (based on Electrocardiographic signals (ECG), Voice, and X-ray techniques) proposed as a diagnostic tool for the accurate detection of COVID-19. The found papers showed high accuracy rate results, ranging between 85.70% and 100%, and F1-Scores from 89.52% to 100%. With this state-of-the-art, we concluded that the models proposed for the detection of COVID-19 already have significant results, but the area still has room for improvement, given the vast symptomatology and the better comprehension of individuals’ evolution of the disease.
- Distinction of different colony types by a smart-data-driven toolPublication . Rodrigues, Pedro Miguel; Ribeiro, Pedro; Tavaria, Freni KekhasharúBackground: Colony morphology (size, color, edge, elevation, and texture), as observed on culture media, can be used to visually discriminate different microorganisms. Methods: This work introduces a hybrid method that combines standard pre-trained CNN keras models and classical machine-learning models for supporting colonies discrimination, developed in Petri-plates. In order to test and validate the system, images of three bacterial species (Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus) cultured in Petri plates were used. Results: The system demonstrated the following Accuracy discrimination rates between pairs of study groups: 92% for Pseudomonas aeruginosa vs. Staphylococcus aureus, 91% for Escherichia coli vs. Staphylococcus aureus and 84% Escherichia coli vs. Pseudomonas aeruginosa. Conclusions: These results show that combining deep-learning models with classical machine-learning models can help to discriminate bacteria colonies with good accuracy ratios.
- Editorial: advances in machine learning approaches and technologies for supporting nervous system disease diagnosisPublication . Rodrigues, Pedro Miguel; Bispo, Bruno Catarino; Freitas, Diamantino; Marques, João Alexandre Lobo; Teixeira, João Paulo
- Electroencephalogram hybrid method for alzheimer early detectionPublication . Rodrigues, Pedro Miguel; Freitas, Diamantino; Teixeira, João Paulo; Bispod, Bruno; Alves, Dílio; Garrett, CarolinaAlzheimer’s disease (AD) is a neurocognitive illness that leads to dementia and mainly affects the elderly. As the percentage of old people is strongly increasing worldwide, it is urgent to develop contributions to solve this complex problem. The early diagnosis at AD first stage known as Mild Cognitive Impairment (MCI) needs a better accuracy and there is not a biomarker able to detect AD without invasive tests. In this study, Electroencephalogram (EEG) signals have been used to serve as a way of finding parameters to improve AD diagnosis in first stages. For that, a hybrid method based on a Cepstral analysis of EEG Discrete Wavelet Transform (DWT) multiband decomposition was developed. Several Cepstral Distances (CD) were extracted to verify the lag between cepstra of conventional bands signals. The results showed that this hybrid method is a good tool for describing and distinguishing the AD EEG activity along its different stages because several statistically significant parameters variations were found between controls, MCI, moderate AD and advanced AD (the lowest p-value=0.003<0.05).
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