Title :
A neural network system to classify simulated ECG rhythms
Author :
Kuppuraj, Ravi Narayan
Author_Institution :
Dept. of Biomed. Eng., Louisiana Tech Univ., Ruston, LA, USA
Abstract :
A neural network system to detect abnormal ECG rhythms is developed. An electronic ECG simulator is used to generate normal and abnormal ECGs, for training and testing the system. The Neural Works Professional II Plus neural network building package was used for this purpose. Also, the performance and efficiency of the neural network for ´delta rule backpropagation´ and ´Widrow-Hoffman rule´ is compared. The delta rule is found to be more efficient in classifying data not encountered in its training phase.
Keywords :
electrocardiography; medical signal processing; neural nets; Widrow-Hoffman rule; abnormal ECG rhythms detection; delta rule backpropagation; electronic ECG simulator; neural network building package; neural network system; simulated ECG rhythms classification; Backpropagation; Electrocardiography; Fibrillation; Frequency; Low pass filters; Neural networks; Pattern recognition; Rhythm; Sampling methods; Testing;
Conference_Titel :
Biomedical Engineering Conference, 1993., Proceedings of the Twelfth Southern
Conference_Location :
New Orleans, LA, USA
Print_ISBN :
0-7803-0976-6
DOI :
10.1109/SBEC.1993.247361
