← Search

Tomás Arias-Vergara

6 accepted papers

2025

A Systematic Evaluation of Machine Learning Methods for Fault Detection and Line Identification in Electrical Power Grids

ICASSP 2025accepted

The integration of renewable energy sources into the electrical grid introduces complex challenges in fault detection and coordination of grid recovery mechanisms. Traditional relay protection systems, which operate based on static rules and predefined thresholds, are inadequate for addressing these…

Cited by 0SourceScholar
2024

Transforming Cardiovascular Health: a Transformer-Based Approach to Continuous, Non-Invasive Blood Pressure Estimation via Radar Sensing

ICASSP 2024accepted

Hypertension is considered to be one of the most critical risk factors for cardiovascular diseases. As such, continuous, accurate and non-invasive monitoring of blood pressure (BP) is of utmost importance and research on such approaches is gaining momentum. In this study, we propose a novel transfor…

Cited by 6SourceScholar
2023

Transferring Quantified Emotion Knowledge for the Detection of Depression in Alzheimer's Disease Using Forestnets

ICASSP 2023accepted

Progressive loss of memory is the most known symptom of Alzheimer’s Disease (AD); however, it also affects other cognitive skills and leads to depression symptoms. This paper presents a transfer learning strategy for automatically detecting AD and depression in AD patients using acoustic information…

Cited by 0SourceScholar
2021

Acoustic and Linguistic Analyses to Assess Early-Onset and Genetic Alzheimer's Disease

ICASSP 2021accepted

The PSEN1-E280A or Paisa mutation is responsible for most of Early-Onset Alzheimer’s (EOA) disease cases in Colombia. It affects a large kindred of over 5000 members that present the same phenotype. The most common symptoms are related to language disorders, where speech fluency is also affected due…

Cited by 0SourceScholar
2021

End-2-End Modeling of Speech and Gait from Patients with Parkinson's Disease: Comparison Between High Quality Vs. Smartphone Data

ICASSP 2021accepted

Parkinson’s disease is a neurodegenerative disorder characterized by the presence of different motor impairments. Speech and gait signals have been analyzed to detect the presence of the disease and the severity in patients. However, most studies have been performed in controlled conditions using hi…

Cited by 0SourceScholar
2018

Unobtrusive Monitoring of Speech Impairments of Parkinson'S Disease Patients Through Mobile Devices

ICASSP 2018accepted

Parkinson's disease (PD) produces several speech impairments in the patients. Automatic classification of PD patients is performed considering speech recordings collected in noncontrolled acoustic conditions during normal phone calls in a unobtrusive way. A speech enhancement algorithm is applied to…

Cited by 0SourceScholar