NSF Award: SenSE program combines sensors and deep machine learning to treat heart failure

With the pandemic a rightful focus worldwide, it is important to know scientists and engineers remain diligent in the treatment of other life-threatening and costly issues.

Congestive heart failure affects nearly six million Americans, with 670,000 diagnosed annually. It remains one of the leading causes of hospital admission, re-admission and death in the United States and is one of the costliest disease syndromes to treat.

Emre Ertin

In this effort, the National Science Foundation just awarded The Ohio State University a three-year $750,000 proposal to team leader and Electrical and Computer Engineering (ECE) Professor Emre Ertin.

The SenSE Program (Multimodal Sensor Systems for Precision Health Enabled by Data Harnessing, Artificial Intelligence, and Learning) team also includes co-investigators Ping Zhang, who shares joint appointments in Ohio State’s Department of Biomedical Informatics (BMI), and the Department of Computer Science and Engineering (CSE), as well as John Fisher, a senior research scientist at MIT.

Ertin said Ohio State ECE postdoctoral researchers, Siddharth Baskar and Nithin Sugavanam, are also “key to the success of the program.”

For NSF, the team outlines a new plan to combine sensors and deep machine learning to not only assess hospitalization risks for congestive heart failure patients, but also factor in patient data from multitude of sources, including Electronic Health Records, to provide a more precise medical regimen.

“In this project, we will pursue proactive approaches to healthcare, supported by innovations in  noninvasive multimodal sensor systems, paired with interpretable deep learning models, for assessing the risk of chronic disease progression,” Ertin said.

ertin.research

Ever rising healthcare costs and the growing population of aging adults with chronic conditions necessitates new predictive, personalized and proactive approaches to cardiovascular health. He said it’s not enough to predict the risk of decompensated heart failure through late symptoms like weight gain and labored breathing.

The SenSE program aims to design, create and validate an easy-to-use sensor patch, combining four key tools to assess real-time cardiac and lung functions: Electrocardiogram (ECG), Bio Radio Frequency (RF), Bio-Impedance, and Seismocardiogram (SCG).

The new technology, Ertin said, reduces the need and high cost of surgeries typically required for implanted monitors, which can result in extended hospital stays.

Ertin said the joint sensor models developed in this project will provide insights into the noninvasive measures related to cardiovascular health, previously only available through implanted sensors and catheterizations in surgery.

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Noninvasive measurements from the sensor patch are then paired with data from a patient’s electronic health records and deep learning models to achieve long-term therapy targets.

“The design of the sensor patch will explore new techniques, by integrating signals from a wide range of frequency bands, into a single flexible board operating autonomously under a power budget,” he said.

The award earned by Ohio State is provided via the Chemical, Bioengineering, Environmental and Transport Systems (CBET) division of NSF. It supports innovative research and education in the fields of chemical engineering, biotechnology, bioengineering, and environmental engineering, and in areas that involve clean and sustainable energy.

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