Publicacoes - INESC TEC

Publicações

Publicações por Bruno Fonseca Coelho

2024

On the feasibility of Vis–NIR spectroscopy and machine learning for real time SARS-CoV-2 detection

Autores
Coelho, BFO; Nunes, SLP; de França, CA; Costa, DdS; do Carmo, RF; Prates, RM; Filho, EFS; Ramos, RP;

Publicação
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

Abstract

2021

High Order Frequency Features as Emotion Discriminators

Autores
Massaranduba, ABR; Coelho, BFO; Sampaio, LR; Ramos, RP;

Publicação
2021 International Conference on e-Health and Bioengineering (EHB)

Abstract

2023

Discrete Representation of Photovoltaic Modules

Autores
Massaranduba, AB; Coelho, B; Machado, E; Silva, E; Pinto, A;

Publicação
IEEE Latin America Transactions

Abstract

2023

Parkinson’s disease effective biomarkers based on Hjorth features improved by machine learning

Autores
Coelho, BFO; Massaranduba, ABR; Souza, CAdS; Viana, GG; Brys, I; Ramos, RP;

Publicação
Expert Systems with Applications

Abstract

2023

Artifact removal for emotion recognition using mutual information and Epanechnikov kernel

Autores
Grilo, M; Moraes, CP; Oliveira Coelho, BF; Massaranduba, ABR; Fantinato, D; Ramos, RP; Neves, A;

Publicação
Biomedical Signal Processing and Control

Abstract

2024

Feature Extraction from EEG signals for detection of Parkinsons Disease

Autores
Souza, C; Viana, G; Coelho, B; Massaranduba, AB; Ramos, R;

Publicação
Anais do XVI Congresso Brasileiro de Inteligência Computacional

Abstract
The Electroencephalogram (EEG) is a medical tool that captures, in a non-invasive way, electrical signals from the brain activities performed by neurons. EEG signals have been the target of study as a biomarker of Parkinsons disease (PD), where several methods of analysis are applied. The present work aims to evaluate features extracted from EEG signals, through methodologies such as HOS, Haralick descriptors, and Fractal Features, as new biomarkers for PD identification. Data from 50 individuals, available at the Open Neuro repository, who underwent an attentional cognitive task were analyzed. RF and SVM algorithms were employed for the classification of the extracted features. The best accuracy achieved was 79.49% in differentiating between Parkinsons subjects and control subjects using Haralick descriptors and RF classifier, suggesting that these features can identify activations in brain areas caused by dopaminergic medication.

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