A5059952617- EEG and Brain-Computer Interfaces
- Neural Networks and Applications
- Sports Performance and Training
- Advanced Control Systems Optimization
- Blind Source Separation Techniques
- Fuzzy Logic and Control Systems
- Fault Detection and Control Systems
- Epilepsy research and treatment
- Sports injuries and prevention
- Neural dynamics and brain function
- Machine Learning in Bioinformatics
- Functional Brain Connectivity Studies
- Control Systems and Identification
- Children's Physical and Motor Development
- Photovoltaic System Optimization Techniques
- ECG Monitoring and Analysis
- Heart Rate Variability and Autonomic Control
- Solar Thermal and Photovoltaic Systems
- Industrial Vision Systems and Defect Detection
- Cardiovascular and exercise physiology
- Microbial Metabolic Engineering and Bioproduction
- Experimental Learning in Engineering
- Physical Education and Gymnastics
- Industrial Technology and Control Systems
- Sports Dynamics and Biomechanics
University of Coimbra
2015-2024
Universidade Estadual de Londrina
2011-2023
ORCID
2021
Hospitais da Universidade de Coimbra
2015
Unidad Ejecutora Lillo
2008-2013
Brazilian Micro and Small Enterprises Support Service
2008
Institute for Systems Engineering and Computers
1999-2001
Laboratoire d'Analyse et d'Architecture des Systèmes
1983-1987
Centre National de la Recherche Scientifique
1987
Abstract The objective of this study was to develop count cut‐points for three different accelerometer models: ActiGraph GT3X, RT3 and Actical accurately classify physical activity intensity levels in adolescents. Seventy‐nine adolescents (10–15 years) participated study. Accelerometers oxygen consumption ( ) data were collected at rest during 11 activities intensities. worn on the waist measured by a portable metabolic system: Cosmed K4b2. Receiver operating characteristic (ROC) curves used...
Summary From the very beginning seizure prediction community faced problems concerning evaluation, standardization, and reproducibility of its studies. One main reasons for these shortcomings was lack access to high‐quality long‐term electroencephalography (EEG) data. In this article we present EPILEPSIAE database, which made publicly available in 2012. We illustrate content scope. The database provides EEG recordings 275 patients as well extensive metadata standardized annotation data sets....
A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensional feature sets, is presented. The combined with classification post-processing schemes aim at the generation of alarms reduced influence false positives. This study considers 216 patients from European Epilepsy Database, includes 185 scalp EEG recordings 31 intracranial data. strategy was tested over a total 16,729.80[Formula: see text]h...
Abstract Seizure prediction may improve the quality of life patients suffering from drug-resistant epilepsy, which accounts for about 30% total epileptic patients. The pre-ictal period determination, characterized by a transitional stage between normal brain activity and seizure, is critical step. Past approaches failed to attain real-world applicability due lack generalization capacity. More recently, deep learning techniques outperform traditional classifiers handle time dependencies....
Abstract Recent evidence suggests that some seizures are preceded by preictal changes start from minutes to hours before an ictal event. Nevertheless adequate statistical evaluation in a large database of continuous multiday recordings is still missing. Here, we investigated the existence long-term intracranial 53 patients with intractable partial epilepsy (in total 531 days and 558 clinical seizures). We describe measure brain excitability based on slow modulation high-frequency gamma...
Abstract The development of seizure prediction models is often based on long-term scalp electroencephalograms (EEGs) since they capture brain electrical activity, are non-invasive, and come at a relatively low-cost. However, suffer from major shortcomings. First, EEG usually highly contaminated with artefacts. Second, changes in the signal over long intervals, known as concept drift, neglected. We evaluate influence these problems deep neural networks using time series shallow widely-used...
The under-19 Brazilian volleyball national team has achieved great performances at international competitions. Because the vertical jump capacity is critical for success in volleyball, purpose of this study was to identify training-induced adaptations on assessed by general and specific tests during 3 different moments (i.e., T1, T2, T3) a macrocycle preparation world championship. sample composed 11 athletes from team-World Champion (age, 18.0 +/- 0.5 years; height: 198.7 5.4 cm; body mass,...
Several studies have reported the phenomenon of post-exercise hypotension. However, factors that cause this drop in blood pressure after a single exercise session are still unknown. To investigate effects aerobic on acute response and to indicators autonomic activity exercise. Ten male subjects (aged 25 ± 1 years) underwent four experimental sessions control cycle ergometer. The heart rate variability each subject were measured at rest 60 min end sessions. Post-exercise hypotension was not...
Seizure prediction might be the solution to tackle apparent unpredictability of seizures in patients with drug-resistant epilepsy, which comprise about a third all epilepsy. Designing seizure models involves defining pre-ictal period, transition stage between inter-ictal brain activity and discharge. This period is typically fixed interval, some recent studies reporting evaluation different patient-specific intervals. Recently, researchers have aimed determine regular seizure. Authors been...
The purpose of the present study was to analyze effects epilepsy on autonomic control heart in pre-ictal phase order find an algorithm early detection seizure onset.Overall 133 epileptic seizures were analyzed from 12 patients with (seven males and five females; mean age 43.91 years, SD: 10.16) participated this study. Single lead electrocardiogram recordings compiled. 240, 90-30, 30-10 5 minutes rate variability (HRV) signals preseizure chosen for analysis rate. As HRV are non-stationary, a...
The Nocturnal Frontal Lobe Epilepsy (NFLE) is a form of epilepsy in which seizures occur predominantly during sleep. In other forms epilepsy, the commonly used clinical approach mainly involves manual inspection encephalography (EEG) signals, laborious and time-consuming process often requires contribution more than one experienced neurologist. last decades, numerous approaches to automate this detection have been proposed and, recently, machine learning has shown very promising performance....
Scalp electroencephalogram (EEG) is a non-invasive measure of brain activity. It widely used in several applications including cognitive tasks, sleep stage detection, and seizure prediction. When recorded over hours, this signal usually corrupted by noisy disturbances such as experimental errors, environmental interferences, physiological artifacts. These may generate confounding factors and, therefore, lead to false results. Models able minimise EEG artifacts are then necessary for...
BACKGROUND: Until now, different approaches have been published to resolve the problem of predicting epileptic seizures. The results are reminiscent a substantial need for improvements in these methods reach stage clinical application. Our aim is develop reliable sei zure prediction algorithm based on Heart Rate Variability (HRV) analysis. METHODS: We analyzed HRV sixteen patients with total 170 seizures, predict occurrence seizures dynamic changes Electrocardiogram (ECG) during pre-ictal...
Abstract Electrocardiogram (ECG) recordings, lasting hours before epileptic seizures, have been studied in the search for evidence of existence a preictal interval that follows normal ECG trace and precedes seizure’s clinical manifestation. The has not yet clinically parametrized. Furthermore, duration this varies seizures both among patients from same patient. In study, we performed heart rate variability (HRV) analysis to investigate discriminative power features HRV identification...
Abstract Typical seizure prediction models aim at discriminating interictal brain activity from pre-seizure electrographic patterns. Given the lack of a preictal clinical definition, fixed interval is widely used to develop these models. Recent studies reporting selection among range intervals show inter- and intra-patient variability, possibly reflecting heterogeneity generation process. Obtaining accurate labels can be train supervised and, hence, avoid setting for all seizures within same...