José Eduardo Krieger
Orcid: 0000-0001-5464-1792
According to our database1,
José Eduardo Krieger
authored at least 21 papers
between 2019 and 2023.
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Bibliography
2023
Frontiers Bioinform., May, 2023
A machine-learning sleep-wake classification model using a reduced number of features derived from photoplethysmography and activity signals.
CoRR, 2023
Machine Learning-Based Diabetes Detection Using Photoplethysmography Signal Features.
CoRR, 2023
Quality Assessment of Photoplethysmography Signals For Cardiovascular Biomarkers Monitoring Using Wearable Devices.
CoRR, 2023
Artificial Intelligence-Driven Screening System for Rapid Image-Based Classification of 12-Lead ECG Exams: A Promising Solution for Emergency Room Prioritization.
IEEE Access, 2023
Blood Pressure Estimation From Photoplethysmography by Considering Intra- and Inter-Subject Variabilities: Guidelines for a Fair Assessment.
IEEE Access, 2023
Automatic segmentation of stroke lesions in T1-weighted magnetic resonance images with convolutional neural networks.
Proceedings of the Medical Imaging 2023: Computer-Aided Diagnosis, 2023
Developing a Machine Learning Pipeline for Predicting Neurological Outcomes in Comatose Cardiac Arrest Survivors Using Continuous EEG Data.
Proceedings of the Computing in Cardiology, 2023
CardioBERTpt: Transformer-based Models for Cardiology Language Representation in Portuguese.
Proceedings of the 36th IEEE International Symposium on Computer-Based Medical Systems, 2023
2022
Proceedings of the Symposium on Internet of Things, 2022
A deep learning approach for COVID-19 screening and localization on chest x-ray images.
Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, 2022
A Machine Learning Approach to Predict Arterial Blood Pressure from Photoplethysmography Signal.
Proceedings of the Computing in Cardiology, 2022
2021
SN Comput. Sci., 2021
Novel Chest Radiographic Biomarkers for COVID-19 Using Radiomic Features Associated with Diagnostics and Outcomes.
J. Digit. Imaging, 2021
CoRR, 2021
A general fully automated deep-learning method to detect cardiomegaly in chest x-rays.
Proceedings of the Medical Imaging 2021: Computer-Aided Diagnosis, 2021
Proceedings of the Medical Imaging 2021: Biomedical Applications in Molecular, 2021
Proceedings of the Computing in Cardiology, CinC 2021, Brno, 2021
2020
Fully Automated Quantification of Cardiac Indices from Cine MRI Using a Combination of Convolution Neural Networks.
Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2020
Multi-View Ensemble Convolutional Neural Network to Improve Classification of Pneumonia in Low Contrast Chest X-Ray Images.
Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2020
2019
J. Chem. Inf. Model., 2019