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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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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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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...
