Use of neural networks for detection of artifacts in arterial pressure waveforms

Two neural net architectures are applied to the problem of detecting artifacts in pulsatile pressure waveforms emanating from catheters in anesthetized patients awaiting cardiac surgery. A three-layer back-propagation network with 21 nodes in each layer satisfactorily detected all artifacts and falsely characterized none of the patterns. A competitive learning network, although easier to build and train, did not perform nearly so well. In both cases, the networks were trained on one set of data and tested on a different set. Both sets of data were taken from an anesthetized patient about to undergo cardiac surgery