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Article
Open AccessAddressing data limitations in seizure prediction through transfer learning
According to the literature, seizure prediction models should be developed following a patient-specific approach. However, seizures are usually very rare events, meaning the number of events that may be used t...
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Article
Open AccessConcept-drifts adaptation for machine learning EEG epilepsy seizure prediction
Seizure prediction remains a challenge, with approximately 30% of patients unresponsive to conventional treatments. Addressing this issue is crucial for improving patients’ quality of life, as timely intervent...
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Article
Open AccessComparison between epileptic seizure prediction and forecasting based on machine learning
Epilepsy affects around 1% of the population worldwide. Anti-epileptic drugs are an excellent option for controlling seizure occurrence but do not work for around one-third of patients. Warning devices employi...
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Article
Open AccessEEG epilepsy seizure prediction: the post-processing stage as a chronology
Almost one-third of epileptic patients fail to achieve seizure control through anti-epileptic drug administration. In the scarcity of completely controlling a patient’s epilepsy, seizure prediction plays a sig...
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Article
Open AccessRemoving artefacts and periodically retraining improve performance of neural network-based seizure prediction models
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. Ho...
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Article
Open AccessEPIC: Annotated epileptic EEG independent components for artifact reduction
Scalp electroencephalogram is a non-invasive multi-channel biosignal that records the brain’s electrical activity. It is highly susceptible to noise that might overshadow important data. Independent component ...
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Article
Open AccessInterpretable EEG seizure prediction using a multiobjective evolutionary algorithm
Seizure prediction might be the solution to tackle the apparent unpredictability of seizures in patients with drug-resistant epilepsy, which comprise about a third of all patients with epilepsy. Designing seiz...
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Chapter and Conference Paper
A Deep Learning Approach for Data-Driven Predictive Maintenance of Rolling Bearings
It is crucial for industrial companies that their systems are available and healthy as most as possible. However, it is inevitable that machines will degrade over time, leading to a fault, or even a complete b...
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Article
Open AccessNational registry for amyotrophic lateral sclerosis: a systematic review for structuring population registries of motor neuron diseases
This article comprises a systematic review of the literature that aims at researching and analyzing the frequently applied guidelines for structuring national databases of epidemiological surveillance for moto...
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Article
Open AccessBiomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review
The use of machine learning (ML) techniques in healthcare encompasses an emerging concept that envisages vast contributions to the tackling of rare diseases. In this scenario, amyotrophic lateral sclerosis (AL...
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Article
Open AccessPrediction of disease progression and outcomes in multiple sclerosis with machine learning
Multiple Sclerosis is a chronic inflammatory disease, affecting the Central Nervous System and leading to irreversible neurological damage, such as long term functional impairment and disability. It has no cur...
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Article
Comparing Different Methods for Named Entity Recognition in Portuguese Neurology Text
Electronic Medical Records (EMRs) are written in an unstructured way, often using natural language. Information Extraction (IE) may be used for acquiring knowledge from such texts, including the automatic reco...
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Chapter and Conference Paper
Automatic Segmentation of Ultrasonic Vocalizations in Rodents
Ultrasonic vocalizations studies in rodents have increasingly drawn researchers attention as it have been considered a powerful tool to understand the animals behavior and their interactions in different soci...
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Chapter and Conference Paper
A Multi-feature Approach for Noise Detection in Lung Sounds
During the acquisition of lung sounds, several sources of noise can interfere with the recordings. Therefore, the detection of noise present in lung sounds plays an important role in the correct diagnosis of s...
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Chapter and Conference Paper
Using the Jupyter Notebook as a Tool to Support the Teaching and Learning Processes in Engineering Courses
Teaching and learning processes can benefit from the use of online resources, enabling the improvement of teachers and students productivity and giving them flexibility and support for collaborative work. Part...
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Chapter and Conference Paper
Named Entity Recognition in Portuguese Neurology Text Using CRF
Automatic recognition of named entities from clinical text lighte...
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Chapter and Conference Paper
Development of an E-learning Platform for Storage, Simulation and Online Experimentation of Models of Physiological Processes
The physiological phenomena are the result of complex interactions between several components in wider biological scales, which is challenging, for the human eye, to understand. Consequently, in biomedical eng...
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Chapter
Brainatic: A System for Real-Time Epileptic Seizure Prediction
A new system developed for real-time scalp EEG-based epileptic seizure prediction is presented, based on real time classification by machine learning methods, and named Brainatic. The system enables the consid...
