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High Performance Reconfigurable Fuzzy Logic Device for Medical Risk Evaluation

Adeoye, Kingsley (2010) High Performance Reconfigurable Fuzzy Logic Device for Medical Risk Evaluation. Master's Thesis, University of Pittsburgh. (Unpublished)

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To date cardiovascular diseases (CVD) account for approximately 35% of all deaths worldwide. Many of these deaths are preventable if the risk of developing them can be accurately assessed early. Medical devices in use today cannot determine a patient's risk of developing a CVD condition. If accurate risk assessment was readily available to doctors, they can track rising trends in risk levels and recommend preventative measures for their patients. If patients had this risk assessment information before symptoms developed or life-threatening conditions occurred, they can contact their doctors to inquire about recommendations or seek help in emergency situations.This thesis research proposes the idea of using evolutionary programmed and tuned fuzzy logic controllers to diagnose a patient's risk of developing a CVD condition. The specific aim of this research seeks to advance the flexibility and functionality of fuzzy logic systems without sacrificing high speed and low resource utilization. The proposed system can be broken down into two layers. The bottom layer contains the controller that implements the fuzzy logic model and calculates the patient's risk of developing a CVD. The controller is designed in a context switchable hardware architecture the can be reconfigured to assess the risk of different CVD diseases. The top layer implements the evolutionary genetic algorithm in software, which configures the fuzzy parameters that optimize the behavior of the controller. The current implementation inputs patient's personal data such as electrocardiogram (ECG) wave features, age and body mass index (BMI) and outputs a risk percentage for Sinus Bradycardia (SB), a common cardiac arrhythmia. We validated this system via Matlab and Modelsim simulations and built the first prototype on a Xilinx Virtex-5 FPGA platform. Experimental results show that this 3-input-1-output fuzzy controller with 5 fuzzy sets per variable and 125 rule propositions produces results within an interval of approximately 1us while reducing hardware resource utilization by at least 25% when compared with existing designs.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairCheng, Allenacc33@pitt.eduACC33
Committee MemberLevitan, Stevenlevitan@pitt.eduLEVITAN
Committee MemberMao, Zhi-Hongzhm4@pitt.eduZHM4
Date: 26 January 2010
Date Type: Completion
Defense Date: 18 September 2009
Approval Date: 26 January 2010
Submission Date: 2 December 2009
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical Engineering
Degree: MSEE - Master of Science in Electrical Engineering
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: ECG; fuzzy logic; cardiovascular disease; FPGA
Other ID:, etd-12022009-122423
Date Deposited: 10 Nov 2011 20:07
Last Modified: 15 Nov 2016 13:52


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