Abstract
A device is described capable of signalling novelty in the state of a system monitored by an arbitrary number of instruments. The device learns by being shown examples only of “healthy” signals from the system and infers the class of “alarm” signals by default. The system is demonstrated on real data acquired from critically ill patients in an intensive care ward and is shown to provide a useful degree of alarm state detection.
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References
David E. Rumelhart and James L. Mcclelland. Learning internal representations by error propagation. In Parallel Distributed Processing: Explorations in the Microstructures of Cognition, volume 1: Foundations, pages 318–362. MIT press, 1986.
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© 1991 Computational Mechanics Publications
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Dodd, N. (1991). Artificial Neural Network for Alarm-State Monitoring. In: Rzevski, G., Adey, R.A. (eds) Applications of Artificial Intelligence in Engineering VI. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-3648-8_41
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DOI: https://doi.org/10.1007/978-94-011-3648-8_41
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-85166-678-2
Online ISBN: 978-94-011-3648-8
eBook Packages: Springer Book Archive