Mauro Pinto

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Invited Assistant Professor

University of Coimbra

Portugal

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About

Mauro Pinto is an Invited Assistant Professor at Universidade de Coimbra, Portugal. His research focuses on the application of machine learning techniques in the prediction of EEG seizures, with recent articles addressing topics such as classifier combination, transfer learning, and the adaptation to concept drifts in seizure prediction models. He aims to enhance the performance and interpretability of prediction systems, particularly for patients with drug-resistant epilepsy.

Recent Grants

Grant: Close

Rumo à previsão realista de crises epilépticas: lidando com desvios de conceito de longo termo e incerteza na rotulagem de dados

Open Date: 2021-01-01

Close Date: 2023-12-31

Grant: Close

PHD Grant

Open Date: 2018-10-01

Close Date:

Articles (17)

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2024

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2024

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2024

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2024

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2023

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2023

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2022

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2022

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2022

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2022

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2021

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2021

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2021

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2020

Collaborators (4)

Sonia Batista

PORTUGAL

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Pedro Martins

Asssistant Professor

University of Coimbra

PORTUGAL

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Antonio Dourado

(Full) Professor

University of Coimbra

PORTUGAL

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César Teixeira

Associate Professor

Faculdade de Ciências da Universidade de Lisboa

PORTUGAL

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Mauro Pinto

Research Interests

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Recent Grants

Articles (17)

Comparison between epileptic seizure prediction and forecasting based on machine learning

Classifier combination supported by the sleep-wake cycle improves EEG seizure prediction performance

On the performance of seizure prediction machine learning methods across different databases: the sample and alarm-based perspectives.

Addressing data limitations in seizure prediction through transfer learning.

Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction.

EEG epilepsy seizure prediction: the post-processing stage as a chronology.

The goal of explaining black boxes in EEG seizure prediction is not to explain models' decisions

Unsupervised EEG preictal interval identification in patients with drug-resistant epilepsy

Removing artefacts and periodically retraining improve performance of neural network-based seizure prediction models.

EPIC: Annotated epileptic EEG independent components for artifact reduction

On the clinical acceptance of black-box systems for EEG seizure prediction

Ensemble Deep Neural Network for Automatic Classification of EEG Independent Components

Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm

A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction

Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy

Automatic Electroencephalogram Artifact Removal Using Deep Convolutional Neural Networks

Prediction of disease progression and outcomes in multiple sclerosis with machine learning

Collaborators (4)

Sonia Batista

Pedro Martins

Antonio Dourado

César Teixeira

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