ECG signal diagnoses using intelligent systems based on FPGA
Dissertant
Thesis advisor
University
University of Technology
Faculty
-
Department
Department of Electrical Engineering
University Country
Iraq
Degree
Master
Degree Date
2012
English Abstract
The heart diseases kill about 17.5 million people per year worldwide.
Thus, they are the main causes of death worldwide, according to the World Heart Federation.
This number is increasing due to inactivity, poor diet, stress, and other common problems in the culture of a globalized capitalist world.
This work presents the use of Particle Swarm Optimization (PSO), neural networks with the most promising supervised learning algorithms for automatic detection of cardiac arrhythmias based on analysis of the electrocardiogram (ECG).
Artificial Neural Network (ANN) has three layers with ten nodes in the input layer, five nodes in the hidden layer and five nodes in the output layer, which is trained using Supervised Learning Algorithms that overcome the bad influence of Over fitting and also trained by using PSO algorithm.
The trained network was able to classify the ECG signal in normal signal, atrial flutter, and ventricular tachycardia, sever conducting tissue and wandering atrial pacemaker.
Field Programmable Gate Arrays (FPGAs) have been used to implement ANN trained by the supervised learning algorithms and PSO, because of their speed benefits, as well as the re-programmability of the FPGAs which can support the reconfiguration necessary to program a neural network.
A VHDL Design of ANN platform is proposed to evolve the architecture ANN circuits using FPGASpartan Family.
The VHDL design platform creates ANN design files using WebPACKTMISE 13.3 program.
All the algorithms used to train the ANN showed high effectiveness with 100% classification.
Main Subjects
Topics
American Psychological Association (APA)
Abd al-Karim, Ali Mundhir. (2012). ECG signal diagnoses using intelligent systems based on FPGA. (Master's theses Theses and Dissertations Master). University of Technology, Iraq
https://search.emarefa.net/detail/BIM-418720
Modern Language Association (MLA)
Abd al-Karim, Ali Mundhir. ECG signal diagnoses using intelligent systems based on FPGA. (Master's theses Theses and Dissertations Master). University of Technology. (2012).
https://search.emarefa.net/detail/BIM-418720
American Medical Association (AMA)
Abd al-Karim, Ali Mundhir. (2012). ECG signal diagnoses using intelligent systems based on FPGA. (Master's theses Theses and Dissertations Master). University of Technology, Iraq
https://search.emarefa.net/detail/BIM-418720
Language
English
Data Type
Arab Theses
Record ID
BIM-418720
