Posada-Quintero, Hugo

LEAD Technologies Inc. V1.01

Assistant Professor Biomedical Engineering

Email h.posada@uconn.edu
Phone 860-486-5099
Mailing Address 260 Glenbrook Road, Unit 3247, University of Connecticut Storrs, CT 06269-3247
Campus Storrs
Google Scholar Link

Brief Bio

Dr. Posada-Quintero’s research includes the development of signal processing techniques, wearable instrumentation, and sensors for biomedical applications. Specifically, the aim of his research is to develop models and biomedical instrumentation for the detection and prediction of stress, fatigue, pain, emotional state, hydration status, wakefulness, cognitive performance, heart failure, among others. He use modern mathematical tools to process bioelectrical signals obtained from different sites of the body, like the electrocardiogram, electromyogram, photoplethysmogram, electrodermal activity, and explore the relationship between those signals and the biomedical variable being detected or predicted. The mathematical processes are focused on the development of more sensitive biomarkers and features, and the development of multimodal algorithms (multiple signals combined). In addition, his use our novel features and train artificial intelligence tools (machine learning and deep learning algorithms) for the development of more accurate models. Furthermore, he develops novel sensors and electronic devices to better capture the electrophysiological signals using portable and wearable devices.

  • Biomedical instrumentation,
  • Biomedical signal processing,
  • Human performance,
  • Human emotions,
  • Biosensors,
  • Neurological disorders.

Current Projects:

1. Objective Integrated Multimodal Electrophysiological Index for the Quantification of Visceral Pain

National Institutes of Health

We aim to establish and validate an objective integrated multimodal electrophysiological index for the level of visceral pain in irritable bowel syndrome patients by integrating surface EDA, ECG, and EMG data into a quantitative diagnostic model through rigorous application of statistical and machine learning algorithms. We will further validate our index by assessing how well it evaluates the treatment efficacy of an IBS personalized pain self-management protocol that we recently established in a clinical trial.

 

2. SEED-57: Evaluating the Effectiveness of Transcranial Direct Current Stimulation for Recovering Cognitive Performance after Sleep Deprivation

DOD/Navy/Office of Naval Research/National Institute for Undersea Vehicle Technology (NIUVT)

We aim to evaluate the effectiveness of transcranial direct current stimulation for recovering cognitive performance after sleep deprivation using a multimodal approach including electrodermal activity and electrocardiogram to understand its physiological effects.

 

3. COMP-68: Sleepiness Detection using Speech and Electrodermal Activity Data

DOD/Navy/Office of Naval Research/National Institute for Undersea Vehicle Technology (NIUVT)

We aim to predict the decline of performance caused by sleep deprivation using speech and electrodermal activity data.

 

4. In-home wearable system to predict the fluid accumulation in acute decompensation heart failure patients

National Institutes of Health

The multidisciplinary team from the University of Massachusetts Amherst, UMass Medical School, and the University of Connecticut propose to develop a novel device for in-home monitoring of HF patients who are at risk of decompensation.

 

5. Objective continuous assessment of nurses' trust in artificial intelligence healthcare technologies

UConn Nursing and Engineering Innovation Center Seed Research Grant Award

 

6. Graph Neural Networks for the detection of Normal Pressure Hydrocephalus from mimics

UConn InCHIP Rolling Seed Grant for Team Formation

 

 

  • BME-6086-Special Topics In Biomedical Engineering: Advanced Biomedical Signal Processing
  • ENGR 1166: Foundations of Engineering
  • UConn Pre-College Summer Program - Artificial Intelligence in Biomedical Engineering
  • Coming soon: First-Year Experience "AI4U".

https://scholar.google.com/citations?user=YJyW4MEAAAAJ&hl=en

https://orcid.org/0000-0003-4514-4772

Scopus Profile

Highlighted Publications:

  1. Posada-Quintero, Hugo F., and Ki H. Chon. Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review. Sensors 20, no. 2 (January 2020): 479.
  2. Posada-Quintero, Hugo F., John P Florian, Alvaro D Orjuela-Cañón, Tomas Aljama-Corrales, Sonia Charleston-Villalobos, Ki H Chon. ‘Power Spectral Density Analysis of Electrodermal Activity for Sympathetic Function Assessment’, Annals of Biomedical Engineering, 44(10), 3124-3135, 2016.
  3. Posada-Quintero, Hugo F., Bruce J. Derrick, M. Claire Ellis, Michael J. Natoli, Christopher Winstead-Derlega, Sara I. Gonzalez, Christopher M. Allen, et al. “Elevation of Spectral Components of Electrodermal Activity Precedes Central Nervous System Oxygen Toxicity Symptoms in Divers.” Communications Medicine 4, no. 1 (December 19, 2024): 1–11.
  4. Mercado-Diaz, Luis R., Yedukondala Rao Veeranki, Fernando Marmolejo-Ramos, and Hugo F. Posada-Quintero. “EDA-Graph: Graph Signal Processing of Electrodermal Activity for Emotional States Detection.” IEEE Journal of Biomedical and Health Informatics, 2024, 1–15.
  5. Golzari, Kia, Youngsun Kong, Sarah A. Reed, and Hugo F. Posada-Quintero. “Sympathetic Arousal Detection in Horses Using Electrodermal Activity.” Animals 13, no. 2 (January 2023): 229.
  6. McNaboe, Riley, Luke Beardslee, Youngsun Kong, Brittany N. Smith, I.-Ping Chen, Hugo F. Posada-Quintero, and Ki H. Chon. “Design and Validation of a Multimodal Wearable Device for Simultaneous Collection of Electrocardiogram, Electromyogram, and Electrodermal Activity.” Sensors 22, no. 22 (January 2022): 8851.