Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/18802
Title: Classification of ECG signals using modified dynamic fuzzy neural network
Authors: Ponnuswamy Mohanapathy Keerthi Ganesh.
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics
Issue Date: 2008
Abstract: The purpose of this research is to assess the condition of a patient’s heart and identify any abnormalities using the Electrocardiogram signal obtained from the patient, with the aid of Modified Dynamic Fuzzy Neural Networks. The Electrocardiography obtained from the patients reveals information about the condition of the patient’s heart by recording the characteristic features of the heart’s electrical activity. The Electrocardiograph needs to be thoroughly examined for the precise identification of heart ailments. This research intends to capture necessary parameters from the electrocardiograph using specific algorithms and classify them effectively and efficiently using hybrid Fuzzy Neural Networks. Various processes that need to be undertaken for classification of ECG signals are reviewed. These are a number of hybrid Fuzzy Neural Networks that can be used for the classification. Different hybrid Fuzzy Neural Networks are studied and their performances evaluated. A comparison between the modified Dynamic Fuzzy Neural Network and the other algorithms is made based on various standard performance indices and the results are tabulated. Efficient algorithms are identified which may be implemented in real-time as ECG analysers, which will aid the cardiologists in detecting abnormalities in the heart with higher degree of accuracy and precision.
URI: http://hdl.handle.net/10356/18802
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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