Dr. Thomas Heldt

Large volumes of heterogeneous data are now routinely collected and archived from patients in a variety of clinical environments, to support real-time decision-making, monitoring of disease progression, and titration of therapy. This rapid expansion of available physiological data has resulted in a data-rich – but often knowledge-poor – environment. Yet the abundance of clinical data also presents an opportunity to systematically fuse and analyze the available data streams, through appropriately chosen mathematical models, and to provide clinicians with insights that may not be readily extracted from visual review of the available data streams.

In this talk, I will highlight our work in model-based signal processing for improved neurocritical care to derive additional and clinically useful information from routinely available data streams. I will present our model-based approach to noninvasive, patient-specific and calibration free estimation of intracranial pressure and highlight how novel algorithms motivate biomedical device innovation.

Biography:

Prof. Heldt received the PhD degree in Medical Physics from the Harvard-MIT Division of Health Sciences and Technology and undertook postdoctoral training at MIT's Laboratory for Electromagnetic and Electronic Systems. Prior to joining the MIT faculty in 2013, Prof. Heldt was a Principal Research Scientist with MIT’s Research Laboratory of Electronics. He currently is an Associate Professor of Electrical and Biomedical Engineering with MIT’s Department of Electrical Engineering and Computer Science, a core faculty member with the Institute for Medical Engineering and Science, and a Principal Investigator with the Research Laboratory of Electronics.

Prof. Heldt’s research interests focus on signal processing, mathematical modeling and model identification in support of real-time clinical decision making, monitoring of disease progression, and titration of therapy, primarily in neurocritical and neonatal critical care. His research is conducted in close collaboration with colleagues at MIT’s Medical Electronic Device Realization Center and clinicians from Boston-area hospitals, where he is integrally involved in designing and deploying data-acquisition systems for clinical data collection.