Importance

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Abstract

Data from a plurality of sensors representing a patient's condition, including measurement signals and also secondary parameters derived from the measurement signals, are displayed in a simple way by calculating a novelty index constituting a one-dimensional visualization space. The novelty index is based on the distance of the current data point in a multi-dimensional measurement space, whose coordinates are defined by the values of the measurement signals and secondary parameters, from a predefined normal point. This may be achieved by using a suitably trained artificial neural network to sum the distance between the current data point in the measurement space and a plurality of prototype points representing normality.

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