Skip to content
Division of Health AIDivision of Health AI
AboutTeamResearchPublicationsInternship
AboutTeamResearchPublicationsInternship
Division of Health AIDivision of Health AI

Clinical AI built with the data and clinicians of one of the largest health systems in the United States.

Explore

  • About
  • Team
  • Research
  • Publications
  • Internship

Affiliations

  • Feinstein Institutes↗ (opens in new tab)
  • Northwell Health↗ (opens in new tab)
  • Zucker School of MedicineHofstra Northwell

Located at

  • Institute of Health System Science
  • Institute of Bioelectronic Medicine
  • Manhasset, New York

© 2026 Division of Health AI, Northwell Health. All rights reserved.

← Back to Team

Researcher · Division of Health AI

Shubham Debnath, PhD

Shubham Debnath is a Senior Research Scientist at the Feinstein Institutes for Medical Research with expertise in autonomic nervous system quantification and bioelectronic medicine. His research focuses on developing methods to modulate the autonomic nervous system function and quantify responses using non-invasive sensors, as well as clinical applications including maternal fever prediction, COVID-19 clinical decision support systems, and analysis of chronic vagus nerve recordings. Debnath has contributed to peer-reviewed publications on autonomic function assessment methodologies and their translational applications in clinical care.

5 projects·8 papers

Shubham Debnath, PhD
Role
Senior Research Scientist
LinkedIn
LinkedIn (opens in new tab)
Google Scholar
Profile (opens in new tab)

Research projects

Research projects

FEVERHRVTEMP

Maternal fever / neonatal sepsis prediction

Continuously monitored vital signs and heart rate variability during labor predict maternal fever 2-3 hours before clinical onset, with area under the curve of 0.748, enabling early detection of mothers at risk for neonatal early-onset sepsis.

Point-of-care AI

SEVEREMODERATECONTROL

PTSD detection from physiological signals

Machine learning models identify PTSD from non-invasive physiological signals, including heart rate variability, in a study currently under review. The lab also studies transcutaneous auricular vagus nerve stimulation as a potential PTSD treatment for World Trade Center responders with limited response to existing therapies.

Autonomic Nervous System AI

M1M2M3M4M5M6CAP

Chronic vagus nerve recordings (mouse)

Chronic wireless recording of compound action potentials from the mouse vagus nerve up to 6 months, enabling longitudinal tracking of neural activity in disease models (CIA, CAIA) to predict inflammation severity and evaluate neuromodulation efficacy.

Preclinical AI

Archive · 2

  • COVID-19 phenotyping & clinical decision support→
  • ANS quantification & non-invasive physiology methods→

Publications

Selected papers

International Journal of Environmental Research and Public HealthMar 2026

Effects of Transcutaneous Auricular Vagus Nerve Stimulation on Posttraumatic Stress Disorder Symptoms in World Trade Center Responders: A Feasibility and Acceptability Study

(opens in new tab)
International Journal of Neural SystemsDec 2025

Longitudinal characterization of compound action potentials in chronic vagus nerve recordings in mice

(opens in new tab)
Digital HealthJan 2023

Prediction of intrapartum fever using continuously monitored vital signs and heart rate variability

(opens in new tab)
International Journal of Environmental Research and Public HealthApr 2022

Understanding Mental Health Needs and Gathering Feedback on Transcutaneous Auricular Vagus Nerve Stimulation as a Potential PTSD Treatment among 9/11 Responders Living with PTSD Symptoms 20 Years Later: A Qualitative Approach

(opens in new tab)
Bioelectronic MedicineAug 2021

A method to quantify autonomic nervous system function in healthy, able-bodied individuals

(opens in new tab)
International IEEE/EMBS Conference on Neural Engineering (NER)May 2021

Noninvasive, multimodal assessment of physiological responses to transcutaneous auricular vagus nerve stimulation

(opens in new tab)
Journal of Clinical Monitoring and ComputingJan 2021

Efficacy of continuous monitoring of maternal temperature during labor using wireless axillary sensors

(opens in new tab)
Bioelectronic MedicineJul 2020

Machine learning to assist clinical decision-making during the COVID-19 pandemic

(opens in new tab)

Related

More of the division

Full roster →

Michael Scheid, PhD

Assistant Investigator, Senior Biomedical Engineer

LC

Lucas Cang

Visiting Scholar

Nabil Ettehadi, PhD

AI/ML Advisor