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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 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 AccessUnsupervised EEG preictal interval identification in patients with drug-resistant epilepsy
Typical seizure prediction models aim at discriminating interictal brain activity from pre-seizure electrographic patterns. Given the lack of a preictal clinical definition, a fixed interval is widely used to ...
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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 AccessHeart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy
Electrocardiogram (ECG) recordings, lasting hours before epileptic seizures, have been studied in the search for evidence of the existence of a preictal interval that follows a normal ECG trace and precedes th...
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Article
Open AccessA personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction
Seizure prediction may improve the quality of life of patients suffering from drug-resistant epilepsy, which accounts for about 30% of the total epileptic patients. The pre-ictal period determination, characte...
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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...
