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, devices and systems for determining the frequency of a periodic physiological process of a subject, particularly respiration rate or heart rate, are disclosed. In one arrangement plural time windows of physiological data are obtained. Reference features corresponding to modulation modes are identified. Modulations of the reference features are extracted. Quality parameters are obtained by processing the extracted modulations. The quality parameters represent how strongly the extracted modulation exhibits a waveform of the periodic physiological process. The extracted modulations are processed to calculate the frequency of the periodic physiological process of the subject.

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, devices and systems for determining the frequency of a periodic physiological process of a subject, particularly respiration rate or heart rate, are disclosed. In one arrangement plural time windows of physiological data are obtained. Reference features corresponding to modulation modes are identified. Modulations of the reference features are extracted. Quality parameters are obtained by processing the extracted modulations. The quality parameters represent how strongly the extracted modulation exhibits a waveform of the periodic physiological process. The extracted modulations are processed to calculate the frequency of the periodic physiological process of the subject.

Abstract: An image of a human, animal or machine subject, is analysed to detect regions which include strong periodic intensity variations, such as a photoplethysmogram (PPG) signal in a human or animal, or some periodic vibration in a machine. The image is divided into plural regions of fixed order is fitted to a representative intensity signal for that region. The poles of the fitted autoregressive model are thresholded by magnitude to select only the pole or poles with a magnitude greater than the threshold. The pole magnitude therefore acts as a signal quality index. The dominant pole is representative of the strongest periodic information and the frequency of that spectral component can be derived from the phase angle of the pole. The image may be redisplayed with image attributes, e.g. color-coding, according to the pole magnitude in each region of interest and/or the dominant pole phase angle in each region of interest. In the case of a PPG image signal this can give maps of heart rate and breathing rate.

Abstract: A method of remote monitoring of vital signs by detecting the PPG signal in an image of a subject taken by a video camera such as a webcam. The PPG signal is identified by auto-regressive analysis of ambient light reflected from a region of interest on the subject's skin. Frequency components of the ambient light and aliasing artefacts resulting from the frame rate of the video camera are cancelled by auto-regressive analysis of ambient light reflected from a region of interest not on the subject's skin, e.g. in the background. This reveals the spectral content of the ambient light allowing identification of the subject's PPG signal. Heart rate, oxygen saturation and breathing rate are obtained from the PPG signal. The values can be combined into a wellness index based on a statistical analysis of the values.

Abstract: A method of obtaining information about the rate of a periodic physiological process from a time series of measurements obtained from a patient, comprising: obtaining the time series of measurements; fitting a model defining a probability distribution over functions to the time series of measurements, wherein the model is defined by a mean function and a periodic covariance function; and outputting the result of the fitting as information about the rate of the periodic physiological process.

Abstract: An image of a human, animal or machine subject, is analysed to detect regions which include strong periodic intensity variations, such as a photoplethysmogram (PPG) signal in a human or animal, or some periodic vibration in a machine. The image is divided into plural regions of fixed order is fitted to a representative intensity signal for that region. The poles of the fitted autoregressive model are thresholded by magnitude to select only the pole or poles with a magnitude greater than the threshold. The pole magnitude therefore acts as a signal quality index. The dominant pole is representative of the strongest periodic information and the frequency of that spectral component can be derived from the phase angle of the pole. The image may be redisplayed with image attributes, e.g. colour-coding, according to the pole magnitude in each region of interest and/or the dominant pole phase angle in each region of interest. In the case of a PPG image signal this can give maps of heart rate and breathing rate.

Abstract: A method of monitoring a system such as a machine, industrial system, or human or animal patient, to classify the system as normal or abnormal, in which a time-series of measurements of the system are regarded as a function to be compared to a model of normality for such functions. The model of normality can be constructed as a Gaussian Process and test functions compared to the model to derive the probability that they are drawn from the model of normality. A probability distribution for the expected extrema of sets of functions drawn from the model can also be derived and the probability of any extremum of a plurality of test functions being an extremum of a set derived from the model of normality can be obtained. The system can be classified as normal or abnormal based on the extreme probability distribution. Test functions with fewer data points can be compared to the model of normality by marginalisation with respect to the missing data points.

Abstract: A method of remote monitoring of vital signs by detecting the PPG signal in an image of a subject taken by a video camera such as a webcam. The PPG signal is identified by auto-regressive analysis of ambient light reflected from a region of interest on the subject's skin. Frequency components of the ambient light and aliasing artefacts resulting from the frame rate of the video camera are cancelled by auto-regressive analysis of ambient light reflected from a region of interest not on the subject's skin, e.g. in the background. This reveals the spectral content of the ambient light allowing identification of the subject's PPG signal. Heart rate, oxygen saturation and breathing rate are obtained from the PPG signal. The values can be combined into a wellness index based on a statistical analysis of the values.

Abstract: 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.