An approach to detect QRS complex using backpropagation neural network
NN'06 Proceedings of the 7th WSEAS International Conference on Neural Networks
The Programmable ECG Simulator
Journal of Medical Systems
Classification of Obstructive Sleep Apnea by Neural Networks
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Unsupervised learning based feature points detection in ECG
ISTASC'08 Proceedings of the 8th conference on Systems theory and scientific computation
Unsupervised learning based feature points detection in ECG
SSIP'08 Proceedings of the 8th conference on Signal, Speech and image processing
A System of Recognition of Characters based on Paraconsistent Artificial Neural Networks
Proceedings of the 2005 conference on Advances in Logic Based Intelligent Systems: Selected Papers of LAPTEC 2005
Semi-supervised Bayesian ARTMAP
Applied Intelligence
A statistical approach for determination of time plane features from digitized ECG
Computers in Biology and Medicine
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The author has developed a self-organizing QRS-wave recognition system for electrocardiograms (ECGs) using neural networks. An ART2 (adaptive resonance theory) network was employed in this self-organizing neural-network system. The system consists of a preprocessor, an ART2, network, and a recognizer. The preprocessor detects R points in the ECG and divides the ECG into cardiac cycles. A QRS-wave is the part of the ECG that is between a Q point and an S point. The input to the ART2 network is one cardiac cycle from which the ART2 network indicates the approximate locations of both the Q and S points. The recognizer establishes search regions for the Q and S points. Then, it locates the Q and S points in each search region. The system uses this method to recognize a QRS-wave. Then, the ART2 network learns the new QRS-wave pattern from the incoming ECG. The ART2 network self-organizes in response to the input ECG. The average recognition error of the present system is less than 1 ms in the recognition of the Q and S points