Brieva, Jorge
Preferred name
Brieva, Jorge
Official Name
Brieva Rico, Jorge Eduardo
Alternative Name
jbrieva
Brieva, J.
Main Affiliation
ORCID
0000-0002-5430-8778
Scopus Author ID
7005997081
Researcher ID
GMW-951-2022
82 results
Now showing 1 - 10 of 82
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Item type:Publication, Automatic Robotics Medication Delivery System: The ANDIS Case Study(Springer Nature Switzerland, 2025-10-11) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Machine Learning Methods in Biomedical Field : Computer-Aided Diagnostics, Healthcare and Biology Applications(Springer Nature Switzerland, 2026); ; ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Neural Architecture Search Using Trajectory Metaheuristics to Classify Coronary Stenosis(IEEE, 2024)8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A non-contact SpO2 estimation using a video magnification technique(2021); ; In this paper, we present a new non-contact strategy to estimate the Peripheral Oxygen Saturation (SpO2) based on the Eulerian motion video magnification technique and a signal processing technique. The magnification procedure was carried out using two approaches: the Hermite decomposition and the Gaussian decomposition. The SpO2 is estimated from the signals extracted after magnification process using the red and the blue channel of the image frame. We have tested the method on five healthy subjects using videos obtained from the googlemeet video conference platform. Each video includes the subject and the data of the contact pulse oximeter device. To compare the performance of the methods, we compute the mean average error and metrics issues from the Bland and Altman analysis to investigate the agreement of the methods with respect to a contact pulse oximeter device as reference. The proposed solution shows an agreement with respect to the reference of most of 98%. These preliminary results are promising for the implementation in a remote medical consultation setting. © 2021 SPIE.Scopus© Citations 3 23 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Bio-Inspired Watermarking Method for Authentication of Fundus Images in Computer-Aided Diagnosis of Retinopathy(MDPI, 2024); ; Lopez-Figueroa, AlbertoNowadays, medical imaging has become an indispensable tool for the diagnosis of some pathologies and as a health prevention instrument. In addition, medical images are transmitted over all types of computer networks, many of them insecure or susceptible to intervention, making sensitive patient information vulnerable. Thus, image watermarking is a popular approach to embed copyright protection, Electronic Patient Information (EPR), institution information, or other digital image into medical images. However, in the medical field, the watermark must preserve the quality of the image for diagnosis purposes. In addition, the inserted watermark must be robust both to intentional and unintentional attacks, which try to delete or weaken it. This work presents a bio-inspired watermarking algorithm applied to retinal fundus images used in computer-aided retinopathy diagnosis. The proposed system uses the Steered Hermite Transform (SHT), an image model inspired by the Human Vision System (HVS), as a spread spectrum watermarking technique, by leveraging its bio-inspired nature to give imperceptibility to the watermark. In addition, the Singular Value Decomposition (SVD) is used to incorporate the robustness of the watermark against attacks. Moreover, the watermark is embedded into the RGB fundus images through the blood vessel patterns extracted by the SHT and using the luma band of Y’CbCr color model. Also, the watermark was encrypted using the Jigsaw Transform (JST) to incorporate an extra level of security. The proposed approach was tested using the image public dataset MESSIDOR-2, which contains 1748 8-bit color images of different sizes and presenting different Diabetic Retinopathy (DR). Thus, on the one hand, in the experiments we evaluate the proposed bio-inspired watermarking method over the entire MESSIDOR-2 dataset, showing that the embedding process does not affect the quality of the fundus images and the extracted watermark, by obtaining average Peak Signal-to-Noise Ratio (PSNR) values higher to 53 dB for the watermarked images and average PSNR values higher to 32 dB to the extracted watermark for the entire dataset. Also, we tested the method against image processing and geometric attacks successfully extracting the watermarking. A comparison of the proposed method against state-of-the-art was performed, obtaining competitive results. On the other hand, we classified the DR grade of the fundus image dataset using four trained deep learning models (VGG16, ResNet50, InceptionV3, and YOLOv8) to evaluate the inference results using the originals and marked images. Thus, the results show that DR grading remains both in the non-marked and marked images. ©MDPIScopus© Citations 1 15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Open Source Implementation for Fall Classification and Fall Detection Systems(2020); ; ; Distributed social coding has created many benefits for software developers. Open source code and publicly available datasets can leverage the development of fall detection and fall classification systems. These systems can help to improve the time in which a person receives help after a fall occurs. Many of the simulated falls datasets consider different types of fall however, very few fall detection systems actually identify and discriminate between each category of falls. In this chapter, we present an open source implementation for fall classification and detection systems using the public UP-Fall Detection dataset. This implementation comprises a set of open codes stored in a GitHub repository for full access and provides a tutorial for using the codes and a concise example for their application. © 2020, Springer Nature Switzerland AG.Scopus© Citations 2 54 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Optical Flow-Hermite and Fuzzy Q-Learning Based Robotic Navigation Approach(2021); ; ; Gomez-Coronel, Sandra L.The present paper presents a bio-inspired optical flow approach to autonomous robotics navigation. It uses a Fuzzy Q-Learning (FQL) method to take decisions in an unknown environment through a reinforcement signal. The proposed method was implemented in a virtual robotics system using the V-REP software and in communication con MATLAB. The preliminary results show that the robot was able to navigate successfully in unknown environments. © 2021 IEEE.Scopus© Citations 1 19 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, SCOMA hand prosthetic(2021)17 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Development of an Electric Powered Assisted Cycle with a Heart Rate Sensor Control System(2023)40 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Computer Vision Approach to Terminus Movement Analysis of Viedma Glacier(2023); ; 14 1