Knowledge-Based Event Detection in Complex Time Series Data

Abstract. This paper describes an approach to the detection of events in complex, multi-channel, high frequency data. The example used is that of detecting the re-siting of a transcutaneous O 2/CO 2 probe on a baby in a neonatal intensive care unit (ICU) from the available monitor data. A software workbench has been developed which enables the expert clinician to display the data and to mark up features of interest. This knowledge is then used to define the parameters for a pattern matcher which runs over a set of intervals derived from the raw data by a new iterative interval merging algorithm. The approach has been tested on a set of 45 probe changes; the preliminary results are encouraging, with an accuracy of identification of 89% 1

Abstracting Steady Qualitative Descriptions over Time from Noisy, High-Frequency Data

On-line monitoring at neonatal intensive care units produces high volumes of data. Numerous devices generate data at high frequency (one data set every second). Both, the high volume and the quite high error-rate of the data make it essential to reach at higher levels of description from suchraw data. These abstractions should improve the medical decision making. We will present a time-oriented data-abstraction method to derive steady qualitative descriptions from ..