Welcome to my professional portfolio! Here, you can find detailed information about my work experience, education, and publications. I am passionate about AI and neurophysiological signals, and this repository is a space where I showcase my journey and contributions.
January 2024 - present
Location: Toledo, Spain
Description: In the FENNSI group, I contributed to various projects, particularly in statistical data analysis and signal processing. However, my primary contribution was to HARIA, a European project focused on using robotic arms to assist physically disabled individuals in their daily activities. I was responsible for integrating the technological systems developed by other research groups into the hospital setting.
January 2024 - September 2024
Location: Madrid, Spain
Description: Title: “Development of Deep Learning methods for brain age prediction in non-human primates”.
This project involved designing a deep learning model, based on a ResNet architecture, for predicting brain age using MRI scan data from marmosets. The work was a collaboration between the University of Cambridge and Universidad Carlos III de Madrid.
Final grade: 8.4/10
November 2022 - January 2024
Location: Alcalá, Spain
Description: The main focus of the research is the application of Artificial Intelligence in diagnosing neurological pathologies. I primarily contributed to two projects:
- Developing deep learning models for diagnosing glaucoma in extreme cases of high myopia, using a database of fundus images. The training data was provided by Miguel Servet Hospital in Zaragoza.
- Designing machine learning models to predict the risk of neurodegenerative diseases, specifically Multiple Sclerosis and Alzheimer’s. The data for this project consisted of retinal layer thickness measurements from patients with these diseases, alongside an equal amount of data from a control group.
May 2022 - November 2022
Location: Madrid, Spain
Description: Collaborated with the Engineering team at TTech to develop machine learning tools for clinical applications.
January 2022 - July 2022
Location: Madrid, Spain
Description: Title: "Development of a method for analyzing neural signals in a subject with neuropathic pain derived from a spinal cord injury".
This project involved the acquisition, processing, and statistical analysis of EEG signals from a motor imagery task in a subject diagnosed with neuropathic pain resulting from a spinal cord injury. The final goal was to analyze if the pain the subject was feeling each day could be related to the brain activity.
Final grade: 9.5/10
2021 - 2022
Location: Toledo, Spain
Description: Collaboration in the project "Monitoring of neuropathic pain in patients with spinal cord injury through a mobile APP and recording of electroencephalographic activity" with the investigation group FENNSI in the National Paraplegic Hospital in Toledo, Spain, and the University of Malmö in Sweden.
The Project was about the development of a mobile APP for accurately recording the pain and other data from the patient. This was used to monitored the subjects that were conducting this experiment.
Master’s degree in Machine Learning in Health at Universidad Carlos III de Madrid (UC3M) (in English) - Universidad Carlos III de Madrid
September 2023 - September 2024
Location: Madrid, Spain
Description: Master's degree focused on the intersection of machine learning and bioengineering, with a strong emphasis on health applications. The program provided in-depth training in data analysis, signal processing, and AI, particularly in medical signals and imaging. This rigorous curriculum equipped me with the theoretical and practical skills necessary for research and R&D roles in industry.
Final grade: 8.38/10
September 2017 - July 2022
Location: Madrid, Spain
Specialization in Data Engineering and Digital Health.
Final grade: 7.4/10
University entrance exam grade: 12.556/14
Diagnosis of multiple sclerosis using optical coherence tomography supported by explainable artificial intelligence
Published in: Eye
Date: 31 January 2024
- Abstract: Optical coherence tomography (OCT) can assist in the early diagnosis of relapsing-remitting multiple sclerosis (RRMS) by analyzing retinal structure. In this study, 79 newly diagnosed RRMS patients and 69 healthy controls were assessed for retinal thickness and inter-eye differences across four retinal layers. Using a Support Vector Machine with Recursive Feature Elimination and Leave-One-Out Cross Validation (SVM-RFE-LOOCV), key diagnostic features were identified, particularly in the papillomacular bundle. A correlation was observed between retinal thinning and functional disability, with greater asymmetry between eyes linked to more severe deterioration. The classifier achieved high sensitivity (0.86) and specificity (0.90), outperforming current diagnostic methods like MRI. This approach provides a robust, explainable tool for assisted diagnosis of RRMS based on OCT data.
- Link: https://www.nature.com/articles/s41433-024-02933-5
- Co-authors: F. J. Dongil-Moreno, M. Ortiz, A. Pueyo, L. Boquete, E. M. Sánchez-Morla, D. Jimeno-Huete, J. M. Miguel, R. Barea, E. Vilades & E. Garcia-Martin
Differential Study of Retinal Thicknesses in the Eyes of Alzheimer’s Patients, Multiple Sclerosis Patients and Healthy Subjects
Published in: Biomedicines
Date: 24 November 2023
- Abstract: Multiple sclerosis (MS) and Alzheimer’s disease (AD) cause retinal thinning that is detectable in vivo using optical coherence tomography (OCT). To date, no papers have compared the two diseases in terms of the structural differences they produce in the retina. The purpose of this study is to analyse and compare the neuroretinal structure in MS patients, AD patients and healthy subjects using OCT. Spectral domain OCT was performed on 21 AD patients, 33 MS patients and 19 control subjects using the Posterior Pole protocol. The area under the receiver operating characteristic (AUROC) curve was used to analyse the differences between the cohorts in nine regions of the retinal nerve fibre layer (RNFL), ganglion cell layer (GCL), inner plexiform layer (IPL) and outer nuclear layer (ONL).
- Link: https://www.mdpi.com/2227-9059/11/12/3126
- Co-authors: Elena Garcia-Martin, Daniel Jimeno-Huete, Francisco J. Dongil-Moreno, Luciano Boquete, Eva M. Sánchez-Morla, Juan M. Miguel-Jiménez,Almudena López-Dorado, Elisa Vilades, Maria I. Fuertes, Ana Pueyo & Miguel Ortiz del Castillo
Published in: Springer
Date: 11 June 2023
- Abstract: Neuropathic pain is a difficult condition to treat and would require reliable biomarkers to personalise and optimise treatments. To date, pain levels are mostly measured with subjective scales, but research has shown that electroencephalography (EEG) and heart rate variability (HRV) can be linked to those levels. Internet of Things technology could allow embedding EEG and HRV in easy-to-use systems that patients can use at home in their daily life. We have developed a system for home monitoring that includes a portable EEG device, a tablet application to guide patients through imaginary motor tasks while recording EEG, a wearable HRV sensor and a mobile phone app to report pain levels. We are using this system in a clinical study involving 15 spinal cord injury patients for one month. Preliminary results show that relevant data are being collected, with inter and intra-patients variability for both HRV and pain levels, and that the mobile phone app is perceived as usable, of good quality and useful. However, because of its complexity, the system requires some effort from patients, is sometimes unreliable and the collected EEG signals are not always of the desired quality.
- Link: https://link.springer.com/chapter/10.1007/978-3-031-34586-9_7
- Co-authors: Dario Salvi, Gent Ymeri, Daniel Jimeno, Vanesa Soto-León, Yolanda Pérez-Borrego, Carl Magnus Olsson & Carmen Carrasco-Lopez
- Programming Languages: Python, Java, MATLAB, C, C++, HTML
- Tools & Frameworks: Git, Docker, SQL
Feel free to reach out to me via:
- Email: [email protected]
- LinkedIn: https://www.linkedin.com/in/daniel-jimeno-huete-800062219/
- GitHub: https://github.com/jimenodaniel7
Thank you for visiting my portfolio! Let’s connect and collaborate.