An embodiment of the invention provides a method of diagnosing obstructive sleep apnea, the method comprising: acquiring a sleep sound signal comprising sounds made by a person during sleep; detecting a plurality of snore sounds in the sleep sound signal; determining a set of mel-frequency cepstral coefficients for each of the snore sounds; determining a characterizing feature for the sleep sound signal responsive to a sum of the variances of the cepstral coefficients; and using the characterizing feature to diagnose obstructive sleep apnea in the person.

A system and method for determining cardiovascular parameters can include: receiving a plethymogram (PG) dataset, removing noise from the PG dataset, segmenting the PG dataset, extracting a set of fiducials from the PG dataset, and transforming the set of fiducials to determine the cardiovascular parameters.

Autoregressive modelling is used to identify periodic physiological signals such as heart rate or breathing rate in an image of a subject. The colour channels of a video signal are windowed and normalised by dividing each signal by its mean. The ratios of the normalised channels to each other are found and principal component analyses conducted on the ratio signals. The most periodic of the principal components is selected and autoregressive models of one or more different orders are fitted to the selected component. Poles of the fitted autoregressive models of different orders are taken and pure sinusoids corresponding to the frequency of each pole are generated and their cross-correlation with the original component is found. Whichever pole corresponds to the sinusoid with the maximum cross-correlation is selected as the best estimate of the frequency of periodic physiological information in the original video signal. The method may be used in a patient monitor or in a webcam-enabled device such as a tablet computer or smart phone.

A heartrate monitor detects heartbeats in a test signal. A local heartrate and an energy of acceleration are associated with the detected heartbeats. Detected heartbeats are included or excluded from a test set of heartbeats based on the local heartrate and energy of acceleration associated with the respective heartbeats. Anomalous heartbeats in the test set of heartbeats are detected using a sparse approximation model. The heartrate monitor may detect heartbeats in a training heartbeat signal. A reference heart rate and an energy of acceleration are associated with detected beats of the training heartbeat signal and selectively included in a set of training data based on the heart rate and energy of acceleration associated with the detected beat in the training heartbeat signal. A dictionary of the sparse representation model may be generated using the set of training data.

An implantable device for implantation in a human body or animal body. The device includes an energy source, an energy storage device, and an electronics unit. Further, an actuator is coupled with the energy storage device and it is configured to emit electromagnetic waves by discharging the energy storage device.

A method and apparatus for estimating the frequency of a dominant periodic component in an input signal by modelling the input signal using auto-regressive models of several different orders to generate candidate frequencies for the periodic component, generating synthetic sinusoidal signals of each of the candidate frequencies, and calculating the cross-correlation of the synthetic signals with the original signal. The frequency of whichever of the synthetic signals has the highest cross-correlation with the original signal is taken as the estimate of the frequency for the dominant periodic component of the input signal. The method may be applied to any noisy signal which has a suspected periodic component, for example physiological signals such as photoplethysmogram signals, and in the estimation of heart rate and breathing rate from such physiological signals.

A machine learning system for evaluating at least one characteristic of a heart valve, an inflow tract, an outflow tract or a combination thereof may include a training mode and a production mode. The training mode may be configured to train a computer and construct a transformation function to predict an unknown anatomical characteristic and/or an unknown physiological characteristic of a heart valve, inflow tract and/or outflow tract, using a known anatomical characteristic and/or a known physiological characteristic the heart valve, inflow tract and/or outflow tract. The production mode may be configured to use the transformation function to predict the unknown anatomical characteristic and/or the unknown physiological characteristic of the heart valve, inflow tract and/or outflow tract, based on the known anatomical characteristic and/or the known physiological characteristic of the heart valve, inflow tract and/or outflow tract.

Medical devices and related systems and methods are provided. A method of estimating a physiological condition involves determining a translation model based at least in part on relationships between first measurement data corresponding to instances of a first sensing arrangement and second measurement data corresponding to instances of a second sensing arrangement, obtaining third measurement data associated with the second sensing arrangement, determining simulated measurement data for the first sensing arrangement by applying the translation model to the third measurement data, and determining an estimation model for a physiological condition using the simulated measurement data, wherein the estimation model is applied to subsequent measurement output provided by an instance of the first sensing arrangement to obtain an estimated value for the physiological condition.

Systems are provided for generating data representing electromagnetic states of a heart for medical, scientific, research, and/or engineering purposes. The systems generate the data based on source configurations such as dimensions of, and scar or fibrosis or pro-arrhythmic substrate location within, a heart and a computational model of the electromagnetic output of the heart. The systems may dynamically generate the source configurations to provide representative source configurations that may be found in a population. For each source configuration of the electromagnetic source, the systems run a simulation of the functioning of the heart to generate modeled electromagnetic output (e.g., an electromagnetic mesh for each simulation step with a voltage at each point of the electromagnetic mesh) for that source configuration. The systems may generate a cardiogram for each source configuration from the modeled electromagnetic output of that source configuration for use in predicting the source location of an arrhythmia.

The present application discloses a method of adjusting a parameter, the parameter being used to derive a physiological characteristic of an individual from an image of the user, the method comprising the steps of: obtaining the parameter for the individual; obtaining a corresponding parameter for a plurality of other individuals within a cohort of the individual; comparing the parameter for the individual with a statistically significant parameter for the plurality of other individuals; and adjusting the parameter for the individual in accordance with the difference between the parameter for the individual and the statistically significant parameter for the plurality of other individuals.