A method of system monitoring or, more particularly, novelty detection, based on extreme value theory in particular a points-over-threshold POT method which is applicable to multimodal multivariate data. Multimodal multivariate data points collected by continuously monitoring a system are transformed into probability space by obtaining their probability density function (pdf) values from a statistical model of normality, such as a pdf fitted to a training data set of normal data. Extremal data is defined as that whose pdf value is below a predetermined threshold and a new analytic function, in particular the Generalised Pareto Distribution (GPD) is fitted to that extremal data only. The fitted GPD can be compared to a GPD fitted to the extremal datapoints of the training data set of normal data to determine if the monitored system is in a normal state. Alternatively a threshold can be set by calculating an extreme value distribution of the GPD fitted to the extremal data of the training data set and setting as the threshold the pdf value which separates a desired proportion, e.g., 0.99 of the probability mass from the remainder. If the minimum pdf value of a set of data points collected from the system is below the threshold, the system may be abnormal.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of system monitoring to automatically detect abnormal states of a system, the method comprising the steps of: (a) repeatedly measuring a plurality of system parameters to produce multi-parameter data points each representing the state of the system at a particular time; (b) comparing each data point to a statistical model giving the probability density function of the normal states of the system to obtain a probability density function value for each data point; and (d) determining whether or not the system state is normal by comparing the obtained probability density function values to a threshold based on a distribution function fitted to those probability density function values of a set of data points known to represent low probability normal states of the system. 2. A method according to claim 1 wherein the step (d) of determining whether or not the system state is normal comprises comparing the distribution of the obtained probability density function values to the fitted distribution function. 3. A method according to claim 1 wherein the step (d) of determining whether or not the system state is normal comprises comparing a distribution function fitted to the obtained probability density function values with the distribution function fitted to those probability density function values of a set of data points known to represent low probability normal states of the system. 4. A method according to claim 3 wherein the set of data points known to represent low probability normal states of the system are selected from a training data set of measurements on the system in a normal state as points which correspond to a probability density function value lower than a first predetermined threshold. 5. A method according to claim 1 wherein the step (d) of determining whether or not the system state is normal comprises comparing the pdf value of the datapoint to a threshold calculated by: fitting a distribution function to the pdf values of a set of data points known to represent low probability normal states of the system, then calculating an extreme value distribution of the fitted distribution function, and setting the threshold on the extreme value distribution as that value which separates a selected proportion of the higher probability mass from the lower probability remainder in the extreme value distribution. 6. A method according to claim 5 wherein the extreme value distribution is calculated by generating a plurality of sets of values from the fitted distribution function, selecting the extremum of each of said sets and fitting an analytic extreme value distribution to the selected extrema. 7. A method according to claim 6 wherein the analytic extreme value distribution is the Weibull distribution. 8. A method according to claim 1 wherein the distribution function is the Generalised Pareto Distribution. 9. A method according to claim 1 wherein the statistical model is multimodal. 10. A method according to claim 1 wherein the statistical model is multivariate, each variable of the statistical model corresponding to one parameter of said multi-parameter data points, each parameter being a measurement of an output of a sensor on the system. 11. A system monitor for monitoring the state of a system in accordance with the method of claim 1, the monitor storing said statistical model and being adapted to perform said repeated measurements of the state of the system to execute said method to classify the system state as normal or abnormal. 12. A system monitor according to claim 11 adapted to acquire measurements of said system state continually and to execute said method on a rolling window of m successive data points. 13. A system monitor according to claim 11 further adapted to store measurements of the system state classified as normal for use in retraining the statistical model. 14. A patient monitor comprising a system monitor according to claim 11 wherein said system is a human patient and said measurements of system parameters comprise measurements of at least two of: heart rate, breathing rate, oxygen saturation, body temperature, systolic blood pressure and diastolic blood pressure.