DocumentCode :
1951963
Title :
Evolvable Block-based Neural Networks for real-time classification of heart arrhythmia From ECG signals
Author :
Nambiar, Vishnu P. ; Khalil-Hani, M. ; Marsono, M.N.
Author_Institution :
Microelectron. & Comput. Eng. Dept., Univ. Teknol. Malaysia, Skudai, Malaysia
fYear :
2012
fDate :
17-19 Dec. 2012
Firstpage :
866
Lastpage :
871
Abstract :
Heart arrhythmia is a fairly common medical condition, in which abnormal electrical activity occurs in the heart. However, it can be life threatening if left untreated or undiagnosed. This paper introduces an improved method to classify heart arrhythmia from electrocardiogram (ECG) signals using Block-based Neural Networks (BbNN). BbNNs are used in the hardware implementation of this problem due to its regular block based structure, relatively fast computational speeds, and lower resource consumption. The training mechanism for evolving BbNNs used in the work utilizes Genetic Algorithm (GA), but is able to handle larger sets of training data more efficiently due to an implementation of a novel multithreaded fitness evaluation approach. The ECG heartbeat dataset is taken from the MIT-BIH arrhythmia database, and feature extraction is done using the evaluation of Hermite polynomials on the preprocessed ECG signal. The proposed BbNN system-on-chip (SoC) shows high accuracy in its arrhythmia classification, with an average accuracy of 99.64% for all tested patient records.
Keywords :
electrocardiography; genetic algorithms; learning (artificial intelligence); medical disorders; medical signal processing; neural nets; signal classification; ECG heartbeat dataset; ECG signals; Hermite polynomial evaluation; MIT-BIH arrhythmia database; abnormal electrical activity; electrocardiogram signals; evolvable block based neural networks; genetic algorithm; hardware implementation; heart arrhythmia classification; heart arrhythmia real time classification; multithreaded fitness evaluation approach; preprocessed ECG signal; training data; training mechanism; Block-based neural networks (BbNNs); electrocardiogram (ECG); genetic algorithm (GA); heart arrhythmia; system-on-chip (SoC);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Engineering and Sciences (IECBES), 2012 IEEE EMBS Conference on
Conference_Location :
Langkawi
Print_ISBN :
978-1-4673-1664-4
Type :
conf
DOI :
10.1109/IECBES.2012.6498165
Filename :
6498165
Link To Document :
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