04/13/2021
By Susan Pryputniewicz

The Biomedical and Biotechnology Graduate Program invites you to attend a doctoral proposal defense by Senait Haileselassie on “Low Cost, Low Foot Print, Fast Processing POC Glucose Meter.”

Ph.D. Candidate: Senait Haileselassie
Date: Tuesday, April 27, 2021
Time: 2 - 3:30 p.m.
Location: In person in Ball Hall, Room 301, North Campus. Those interested in attending should contact Senait_Haileselassie@uml.edu and committee advisor Mufeed_MahD@uml.edu at least 48 hours prior to the defense to request attendance at the meeting. Due to social distancing requirements, attendance is limited.

Committee Chair (Advisor): Mufeed MahD, Ph.D., Associate Professor, Department of Electrical and Computer Engineering, University of Massachusetts Lowell

Committee Members:

  • Samson Mil’shtein, Ph.D., Professor, Department of Electrical and Computer Engineering, University of Massachusetts Lowell
  • Bryan Buchholz, Ph.D., Professor, Department of Biomedical Engineering, University of Massachusetts Lowell

Abstract:
According to the American Diabetes Association, 88 million adults (34.5% of the adult US population) aged 18 years or older have prediabetes out of which 24.2 million are aged 65 years or older. Despite 34.2 million people have diabetes (10.5% of the US population), only 26.9 million people are diagnosed with diabetes, and 7.3 million people are not diagnosed. Diabetes has Impact on social, economic, and ethnic background. The challenge is not delivering the insulin, it is the method that is used to sense the glucose level. As a result, this PhD research is focused on building a continuous glucose monitoring to inject accurate dose of insulin as needed.

A noninvasive blood glucose measuring device is proposed, implemented (beta model) and tested. The device belongs to the class of continuous glucose monitor with cloud connectivity. It is meant to be POC, easy to operate, take into consideration demographic factors such as color, race, and gender. The device is to be built sturdy and reliable.

The device takes burst of measurements, apply some statistical and digital signal processing to output impedance frequency spectrum. Correlation coefficient, NRMSE (normalized root mean squared error), MAPE (mean absolute percent error) are calculated. Device sensitivity and selectivity to some parameters variation were tested and proved accurate.

The device is to use glucose oxidase enzyme employing a human phantom developed at UML.