Percorrer por autor "Ribeiro, P."
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- Alzheimer’s detection through structural MRI texture analysisPublication . Oliveira, M. J.; Ribeiro, P.; Rodrigues, P. M.Introduction: Alzheimer’s Disease (AD) is the most common cause of dementia. It affects mostly the elderly and is directly impacted by the observed growth of life expectancy. AD manifests as a chronic and progressive neurodegenerative disease, gradually deteriorating memory and cognitive abilities, and diminishing day-to-day quality of life. As the global population ages, understanding and addressing the challenges of AD becomes increasingly important for public health. Early detection enables treatment planning and symptom management, becoming an important study subject. In that sense, the present study aims to develop an automatic Structural MRI-based tool for the detection of AD and early stages of the disease (Mild Cognitive Impairment—MCI). Methods: 504 pre-processed sMRI images were decomposed into slices comprising the three anatomical planes (axial, coronal and sagittal) from where a set of 22 GLCM features were computed to feed 18 machine learning models, employing a hold-out method (80-20 train--test split). The analysis involved comparing three classes, HC (Healthy Controls), MCI and moderate AD in an All vs. All classification approach. Results and Discussion: A wide set of metrics was used to evaluate the model's performance. Combining the three anatomical planes, the All vs. All classification with a Linear Support Vector Machine yielded the following results: 82.2% for Accuracy, 82.2% for Recall, 83.0% for Precision, 89.9% Specificity, 81.9% for F1-Score and 89.8% for AUC. Conclusions: The results indicate that the proposed model distinguishes between AD, CN and MCI well. The methodology used provided a balanced performance across the seven metrics, highlighting the model's robustness and reliability in classifying the different groups. This approach shows significant potential for aiding in AD early detection and diagnosis and related cognitive impairments with an unusual approach.
- Are there any Pinus pinaster trees resistant to Bursaphelenchus xylophilus? Studies implemented in Portugal to address this questionPublication . Costa, R.; Ribeiro, P.; Evaristo, I.; Ribeiro, B.; Aguiar, A.; Carrasquinho, I.; Santos, C.; Vasconcelos, Marta W.
- Assessment of the post-acute covid-19 syndrome cardiovascular effect through ECG analysisPublication . Ribeiro, P.; Souza, C. C. C. D.; Camerino, C. M. C.; Pordeus, D.; Leite, C. F.; Marques, J. A. L.; Madeiro, J. P.; Rodrigues, P. M.Introduction: SARS-CoV-2, a virus responsible for the emergence of the life-threatening disease known as COVID-19, exhibits a diverse range of clinical manifestations. The spectrum of symptoms varies widely, encompassing mild to severe presentations, while a considerable portion of the population remains asymptomatic. COVID-19, primarily a respiratory virus, has been linked to cardiovascular complications in some patients. Notably, cardiac issues can also arise after recovery, contributing to post-acute COVID-19 syndrome, a significant concern for patient health. The present study intends to evaluate the post-acute COVID-19 syndrome cardiovascular effect through ECG by comparing patients affected with cardiac diseases without COVID-19 diagnosis report (class 1) and patients with cardiac pathologies who present post-acute COVID-19 syndrome (class 2). Methods: From 2 body positions, a total of 10 non-linear features, extracted every 1 second under a multi-band analysis performed by Discrete Wavelet Transform (DWT), have been compressed by 6 statistical metrics to serve as inputs for an individual feature analysis by the means of Mann-Whitney U-test and XROC classification. Results and Discussion: 480 Mann-Whitney U-test statistical analyses and XROC discrimination approaches have been done. The percentage of statistical analysis with significant differences (p<0.05) was 30.42% (146 out of 480). The best overall results were obtained by approximating the feature Energy, with the data compressor Kurtosis in the body position Down. Those results were 83.33% of Accuracy, 83.33% of Sensitivity, 83.33% of Specificity and 87.50% of AUC. Conclusions: The results show that the applied methodology can be a way to show changes in cardiac behaviour provoked by post-acute COVID-19 syndrome.
- Speech non-linear multiband-time-series analysis for detecting Alzheimer’s diseasePublication . Silva, M. G.; Ribeiro, P.; Bispo, B. C.; Rodrigues, P. M.Introduction: Alzheimer’s Disease (AD) is a prevalent neurodegenerative disorder, anticipated to triple in cases by 2050. It constitutes 50-75% of dementia cases and currently lacks a cure. Early diagnosis is crucial, allowing for treatments that may delay its progression. Traditional diagnostic methods, though effective, are invasive and expensive. Speech signal analysis has emerged as a promising non-invasive, cost-effective alternative for early AD diagnosis. Methods: This study investigates the application of non-linear analysis under a Discrete Wavelet Transform (DWT) of speech signals for detecting AD stages. The dataset comprises 360 audio recordings from the DementiaBank Spanish Ivanova Corpus, categorized into AD, Mild Cognitive Impairment (MCI), and healthy control groups. The 360 speech signals were cleaned by removing artifacts through a filter and moments of silence utilizing Voice Activity Detection (VAD). A 50% overlap rectangular sliding window process of a 5-second duration was used, and within each window, the signal was decomposed by DWT into six bands. From each band, 10 non-linear parameters analyze the complex dynamics of our speech signals. Each feature time series is compressed over time per band, utilizing six compression metrics, and the resulting data are divided into groups based on gender and AD stage. Classical machine learning classification was implemented, and an iterative application of various normalization, feature selection, and optimization techniques was employed. The final step tested 20 classifiers to determine the most effective model for discrimination between groups. Results and Discussion: Our findings show a 100% accuracy between men with AD and women with AD, healthy men and women with AD, and men with AD and healthy women. Furthermore, nearly all of our 15 group comparisons have an accuracy of higher than 90.9%. Conclusion: In conclusion, our techniques culminated in a model that achieved good model performance and could differentiate between men and women, and between the three studied stages of AD.