Oxford Biosignals Limited Patent applications |
Patent application number | Title | Published |
20100298730 | METHOD AND APPARATUS FOR MEASURING BREATHING RATE - An example method and apparatus for measuring the breathing rate from the photoplethysmogram signal (PPG) uses auto-regressive modelling of the signal. The PPG signal is windowed in overlapping windows of typically 30 seconds' length, overlapping by 25 seconds, to obtain discrete sections of the signal and each section is low-pass filtered, downsampled and detrended and then AR modelled using an all-pole auto-regressive (AR) model. The AR model allows identification of the dominant frequencies in the signal and the pole corresponding to the breathing rate is identified by considering its magnitude and the breathing rate it represents. Each 30 second window gives a breathing rate estimate and use of successive windows displaced by 5 seconds results in a breathing rate estimate every 5 seconds. The time series of breathing rate estimates can be Kalman filtered to reject measurements which have a large change in magnitude or represent a large change in breathing rate. The measurements may also be fused with measurements from another sensor. | 11-25-2010 |
20100056939 | METHOD OF BIOMEDICAL SIGNAL ANALYSIS INCLUDING IMPROVED AUTOMATIC SEGMENTATION - A method of analysing biomedical signals, for example electrocardiograms, by using a Hidden Markov Model for subsections of the signal. In the case of an electrocardiogram two Hidden Markov Models are used to detect respectively the start and end of the QT interval. The relationship between the QT interval and heart rate can be computed and a contemporaneous value for the slope of this relationship can be obtained by calculating the QT/RR relationship for all of the beats in a sliding time window based on the current beat. Portions of electrocardiograms taken on different days can efficiently and accurately be compared by selecting time windows of the ECGs at the same time of day, and looking for similar beats in those time windows. | 03-04-2010 |
20100049069 | BIOMEDICAL SIGNAL MORPHOLOGY ANALYSIS METHOD - A way of quantifying the shape of an ECG waveform is disclosed by detecting the JT segment using two Hidden Markov Models and calculating the analytic signal of the JT segment. Parameters calculated from the analytic signal are used as shape descriptors for the JT segment. The shape descriptors may be displayed in a dimensionality-reduced mapping. Templates representing characteristic shapes can be produced by finding cluster centres in the shape descriptor space, and the novelty of new waveforms can be quantified by comparing the position in shape descriptor space of new shape descriptors to a predefined normal training set or to previously encountered waveforms. Novel shape descriptors can be used to retrieve the corresponding waveforms, and templates of such novel shapes can be created by averaging such waveforms, using dynamic time warping to allow for variations in heart rate. The templates can be manually segmented and the manual segmentation propagated back into other waveforms having similar shape descriptors. | 02-25-2010 |
20090187381 | Novelty detection - A method and apparatus for detecting an abnormality in e.g. in operating characteristics or function of a machine, apparatus or system, the method including providing a data sample set comprising n values of a measured physical parameter associated with the apparatus or system generated by repeating a measurement of the physical parameter n times. An extremal measured parameter value is selected from amongst the data sample set, determining a probability of observing the selected parameter value (e.g. of observing a value not exceeding the selected parameter value) by applying the selected parameter value to an extreme value probability distribution function having a location parameter and a scale parameter. The value of the location parameter and the value of the scale parameter are each constructed using an integer value m (e.g. notionally representing the size of a sub-sample data set comprising m of said measured parameter values) in which m is less than n (i.e. m07-23-2009 | |