Abstract: System monitors and methods of monitoring a system are disclosed. In one arrangement a system monitor predicts a future state of a system. A data receiving unit receives system data representing a set of one or more measurements performed on the system. A first statistical model is fitted to the system data. The first statistical model is compared to each of a plurality of dictionary entries in a database. Each dictionary entry comprises a second statistical model. The second statistical model is of the same general class as the first statistical model and obtained by fitting the second statistical model to data representing a set of one or more previous measurements performed on a system of the same type as the system being monitored and having a known subsequent state. A prediction of a future state of the system being monitored is output based on the comparison. The first statistical model and the second statistical model are each a stochastic process or approximation to a stochastic process.

Abstract: Methods and apparatus for monitoring a human or animal subject are disclosed. In one arrangement, test data representing a time-series of physiological measurements performed on a subject in a measurement session is received. A mean trajectory with error bounds is fitted to the test data. A state of the subject is determined by comparing the fitted mean trajectory with error bounds to a stored model of normality. The stored model of normality comprises a library of latent mean trajectories with error bounds. Each latent mean trajectory with error bounds is derived by fitting a hierarchical probabilistic model to a respective one of a plurality of sets of historical data. Each set of historical data comprises a plurality of session data units. Each session data unit representing a time-series of physiological measurements obtained during a different measurement session.

Abstract: System monitors and methods of monitoring a system are disclosed. In one arrangement a system monitor predicts a future state of a system. A data receiving unit receives system data representing a set of one or more measurements performed on the system. A first statistical model is fitted to the system data. The first statistical model is compared to each of a plurality of dictionary entries in a database. Each dictionary entry comprises a second statistical model. The second statistical model is of the same general class as the first statistical model and obtained by fitting the second statistical model to data representing a set of one or more previous measurements performed on a system of the same type as the system being monitored and having a known subsequent state. A prediction of a future state of the system being monitored is output based on the comparison. The first statistical model and the second statistical model are each a stochastic process or approximation to a stochastic process.