Research Component of Phd Qualifier Exam by: Patrick R. DaSilva
When: Friday,
April 28, 2017
11:00 AM
-
1:00 PM
Where: Science & Engineering Building, Lester W. Cory Conference Room: Room 213A
Cost: Free
Description: Topic: Electrocardiogram Collection, Pattern Recognition and Classification System Supporting a Cardiovascular Disease Detection Aid
Location: Lester W. Cory Conference Room
Science & Engineering Building (SENG), Room 213A
Abstract:
The leading cause of mortality in the United States is heart disease. According to the CDC
Division of Vital Statistics, 26% of total death tolls (2,423,712) in 2007 were directly related to heart diseases. Preliminary 2009 data shows that this lethal disease continued to be the main cause of death for 598,607 Americans. Affected patients usually do not comprehend how their disease progress because they are not given the proper training upon hospital discharge to recognize how subtle changes have a large effect on their overall health. There is a financial strain on our healthcare system and it's important for patients to be knowledgeable in self-care in order to assess and take appropriate action before symptoms become intense.
Current mobile monitoring solutions do not offer the ability to recognize cardiac problems without the use of an outside technician. In support of cardiovascular disease detection aid set forth by Dr. Paul Fortier of UMASS Dartmouth, an attempt is made to perform pattern recognition and classification on a real-time electrocardiogram (EKG). The EKG is detected through the use of the Wavelet Transform and the rhythm is classified using an N-ary tree. The entire software platform is designed and developed in the C language. It's further loaded and tested on an Atmel microcontroller. Testing results showed that autonomous classifications are possible using a three lead system while the patient is at rest. Further research is required to compensate for muscle noise and motion artifacts, but the proposed classification method serves as a stepping stone towards a fully reliable teaching tool with the potential to serve as a solution to the current cardiovascular healthcare issue.
Note: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.
Advisor: Dr. Paul J. Fortier
Committee Members: Dr. Honggang Wang and Dr. Liudong Xing, Department of Electrical & Computer Engineering and Dr. Kristen Sethares, College of Nursing
*For further information, please contact Dr. Paul J. Fortier at 508.999.8544, or via email at pfortier@umassd.edu
Location: Lester W. Cory Conference Room
Science & Engineering Building (SENG), Room 213A
Abstract:
The leading cause of mortality in the United States is heart disease. According to the CDC
Division of Vital Statistics, 26% of total death tolls (2,423,712) in 2007 were directly related to heart diseases. Preliminary 2009 data shows that this lethal disease continued to be the main cause of death for 598,607 Americans. Affected patients usually do not comprehend how their disease progress because they are not given the proper training upon hospital discharge to recognize how subtle changes have a large effect on their overall health. There is a financial strain on our healthcare system and it's important for patients to be knowledgeable in self-care in order to assess and take appropriate action before symptoms become intense.
Current mobile monitoring solutions do not offer the ability to recognize cardiac problems without the use of an outside technician. In support of cardiovascular disease detection aid set forth by Dr. Paul Fortier of UMASS Dartmouth, an attempt is made to perform pattern recognition and classification on a real-time electrocardiogram (EKG). The EKG is detected through the use of the Wavelet Transform and the rhythm is classified using an N-ary tree. The entire software platform is designed and developed in the C language. It's further loaded and tested on an Atmel microcontroller. Testing results showed that autonomous classifications are possible using a three lead system while the patient is at rest. Further research is required to compensate for muscle noise and motion artifacts, but the proposed classification method serves as a stepping stone towards a fully reliable teaching tool with the potential to serve as a solution to the current cardiovascular healthcare issue.
Note: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.
Advisor: Dr. Paul J. Fortier
Committee Members: Dr. Honggang Wang and Dr. Liudong Xing, Department of Electrical & Computer Engineering and Dr. Kristen Sethares, College of Nursing
*For further information, please contact Dr. Paul J. Fortier at 508.999.8544, or via email at pfortier@umassd.edu
Contact:
ECE: Electrical & Computer Engineering Department 508.999.9164 http://www.umassd.edu/engineering/ece/
Topical Areas: General Public, University Community, College of Engineering, Electrical and Computer Engineering, Students