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
Intelligent Patient Monitoring: From Hardware to Learnware
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part I: Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira's Scientific Legacy
On the design of a multimodal cognitive architecture for perceptual learning in industrial robots
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Automatic Classification of Heartbeats Using Wavelet Neural Network
Journal of Medical Systems
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This paper describes MART, an ART-based neural network for adaptive classification of multichannel signal patterns without prior supervised learning. Like other ART-based classifiers, MART is especially suitable for situations in which not even the number of pattern categories to be distinguished is known a priori; its novelty lies in its truly multichannel orientation, especially its ability to quantify and take into account during pattern classification the different changing reliability of the individual signal channels. The extent to which this ability can reduce the creation of spurious or duplicate categories (a major problem for ART-based classifiers of noisy signals) is illustrated by evaluation of its performance in classifying QRS complexes in two-channel ECG traces which were taken from the MIT-BIH database and contaminated with noise