Brieva, Jorge
Main Affiliation
Preferred name
Brieva, Jorge
Official Name
Brieva Rico, Jorge Eduardo
ORCID
0000-0002-5430-8778
Researcher ID
GMW-951-2022
Scopus Author ID
7005997081
86 results
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Item type:Publication, Image Facial Expression Recognition based on Active Muscles and their Notable Triangle Points(IEEE, 2025); During emotion experience originated in psychological changes, the effect in face muscles results in a characteristic set of contractions associated to specific emotions. This paper propose an intuitive representation of these interactions with the objective of facial expression recognition through geometric features. In medical research, it has generated insights regarding emotional state, cognitive function, and pain level during clinical procedures leading to an effective patient treatment, assisting diagnosis and monitoring disease progression mainly in neurological conditions. Starting from a facial muscle modeling using triangles, it utilizes an initial 68 landmarks fitting algorithm, and later the computation of triangle notable points to work as anchors of specific muscles. Secondly, the optimization process through stochastic techniques is applied to set the point type combination so that the F1-Score is maximized. Experimental results were performed with conventional classifiers and no fine tuning, accomplishing an accuracy, precision, recall and F1-score of 0.88 for KDEF dataset, while 0.84, 0.86, 0.84, and 0.84 respectively for the JAFFE dataset, proving to be a reliable technique in the expression recognition problem. ©The authors ©IEEE. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Preface : Proceedings of the 21st International Symposium on Medical Information Processing and Analysis (SIPAIM)This year’s meeting included keynote lectures by five recognized experts who addressed advances in neuroimaging and computational methods for medical applications. The keynote talks covered topics including PET imaging in Alzheimer’s disease and its role in understanding disease progression and supporting the development of new therapies; large-scale global collaboration through the integration of neuroimaging data from around the world; implicit neural models for representing medical images, spanning reconstruction to shape analysis; and a data-driven approach to the management of craniosynostosis. In addition, the conference hosted special clinical sessions in ophthalmology, pathology, and gastroenterology, focusing on the application of artificial intelligence to support clinical decision-making in these domains. The editors would like to thank the authors, reviewers and committee members, without whom the publication of the present volume would not have been possible. Likewise, we would like to thank IEEE for publishing our proceedings in IEEE Xplore. Last but not least, we are grateful to all our sponsors for both financial and logistical contributions: Universidad de Nariño, Children’s National Hospital, the SIPAIM Society, Pontificia Universidad Javeriana, Galileo University, Voxel Healthcare, and for the technical co-sponsorship by the IEEE Engineering in Biology and Medicine Society, IEE Signal Processing and the endorsement by the Medical Image Computing and Computer Assisted Intervention (MICCAI) society. ©The authors ©IEE. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Preface : Machine Learning Methods in Biomedical Field Computer-Aided Diagnostics, Healthcare and Biology Applications(Springer Science and Business Media Deutschland GmbH, 2026); ; ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Metaheuristic Strategy in Automatic Robotics Navigation for Patient Care in Hospitals(Springer Nature Switzerland, 2025); ; This chapter presents a novel approach for developing a global automatic navigation strategy for a mobile robot for patient care into health centers and hospitals. The proposal uses a metaheuristic strategy through the Grey Wolf Optimizer algorithm to find the shortest route between the robot’s starting and patient destination points, while also integrating knowledge of the environment acquired through vision. The global navigation tracking is achieved through the use of Fuzzy Logic Rules to control the robot’s wheel velocity according to its current position and orientation. The proposed strategy was implemented in a virtual robotics environment demonstrating that the approach successfully generated optimal paths for various environments with a minimal number of control points and in a relatively short amount of time. In addition, a simulated hospital room with common furniture and tasks was used to evaluate the performance of the global navigation strategy, demonstrating that the robot could successfully generate a global route and navigate within this environment. ©The authors ©Springer. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Challenges and Advances in Digital Processing of Fetal Phonocardiography Signal: A Review(Springer Nature Switzerland, 2025); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Non-Contact Oxygen Saturation Estimation Using Deep Learning Ensemble Models and Bayesian OptimizationMonitoring Peripheral Oxygen Saturation (SpO2) is an important vital sign both in Intensive Care Units (ICUs), during surgery and convalescence, and as part of remote medical consultations after of the COVID-19 pandemic. This has made the development of new SpO2-measurement tools an area of active research and opportunity. In this paper, we present a new Deep Learning (DL) combined strategy to estimate SpO2 without contact, using pre-magnified facial videos to reveal subtle color changes related to blood flow and with no calibration per subject required. We applied the Eulerian Video Magnification technique using the Hermite Transform (EVM-HT) as a feature detector to feed a Three-Dimensional Convolutional Neural Network (3D-CNN). Additionally, parameters and hyperparameter Bayesian optimization and an ensemble technique over the dataset magnified were applied. We tested the method on 18 healthy subjects, where facial videos of the subjects, including the automatic detection of the reference from a contact pulse oximeter device, were acquired. As performance metrics for the SpO2-estimation proposal, we calculated the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and other parameters from the Bland–Altman (BA) analysis with respect to the reference. Therefore, a significant improvement was observed by adding the ensemble technique with respect to the only optimization, obtaining 14.32% in RMSE (reduction from 0.6204 to 0.5315) and 13.23% in MAE (reduction from 0.4323 to 0.3751). On the other hand, regarding Bland–Altman analysis, the upper and lower limits of agreement for the Mean of Differences (MOD) between the estimation and the ground truth were 1.04 and −1.05, with an MOD (bias) of −0.00175; therefore, MOD ±1.96𝜎 = −0.00175 ± 1.04. Thus, by leveraging Bayesian optimization for hyperparameter tuning and integrating a Bagging Ensemble, we achieved a significant reduction in the training error (bias), achieving a better generalization over the test set, and reducing the variance in comparison with the baseline model for SpO2 estimation. ©The authors ©Technologies ©MDPI. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Orthosis Control Based on Electromyographic Signals and Machine Learning(Springer Nature Switzerland, 2025) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Design of an Underwater Mechatronics System for Detecting Dissolved Solids in Water(IEEE, 2024) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Deep-Learning and Bio-inspired Vision Model-Based Approach for Automatic Coronary Arteries Segmentation(IEEE, 2025); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Complexity-Driven Adversarial Validation for Corrupted Medical Imaging DataDistribution shifts commonly arise in real-world machine learning scenarios in which the fundamental assumption that training and test data are drawn from independent and identically distributed samples is violated. In the case of medical data, such distribution shifts often occur during data acquisition and pose a significant challenge to the robustness and reliability of artificial intelligence systems in clinical practice. Additionally, quantifying these shifts without training a model remains a key open problem. This paper proposes a comprehensive methodological framework for evaluating the impact of such shifts on medical image datasets under artificial transformations that simulate acquisition variations, leveraging the Cumulative Spectral Gradient (CSG) score as a measure of multiclass classification complexity induced by distributional changes. Building on prior work, the proposed approach is meaningfully extended to twelve 2D medical imaging benchmarks from the MedMNIST collection, covering both binary and multiclass tasks, as well as grayscale and RGB modalities. We evaluate the metric analyzing its robustness to clinically inspired distribution shifts that are systematically simulated through motion blur, additive noise, brightness and contrast variation, and sharpness variation, each applied at three severity levels. This results in a large-scale benchmark that enables a detailed analysis of how dataset characteristics, transformation types, and distortion severity influence distribution shifts. Thus, the findings show that while the metric remains generally stable under noise and focus distortions, it is highly sensitive to variations in brightness and contrast. On the other hand, the proposed methodology is compared against Cleanlab’s widely used Non-IID score on the RetinaMNIST dataset using a pre-trained ResNet-50 model, including both class-wise analysis and correlation assessment between metrics. Finally, interpretability is incorporated through class activation map analysis on BloodMNIST and its corrupted variants to support and contextualize the quantitative findings. ©The authors ©MDPI.