Key result
MART, a multichannel ART-based neural network, reduces the creation of spurious or duplicate categories when classifying QRS complexes in noisy two-channel ECG traces.
MART is a novel neural network algorithm that improves the adaptive classification of multichannel ECG signals by accounting for varying channel reliability.
May enhance noisy multichannel ECG classification reliability; leaves open prospective clinical validation before practice change.
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.
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Manuel Fernández-Delgado (1998) studied QRS complexes in noisy ECG traces. MART (multichannel ART-based neural network) was evaluated on Performance in classifying QRS complexes and reduction of spurious or duplicate categories. MART, a multichannel ART-based neural network, reduces the creation of spurious or duplicate categories when classifying QRS complexes in noisy two-channel ECG traces.
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