Philips Intellectual Property & Standards

There is provided a computer-implemented method comprising receiving subjective sleep time data representative of a subjective sleep time of a subject over multiple days; receiving objective sleep time data representative of an objective sleep time of the subject over the multiple days; determining a sleep time misperception based on the subjective sleep time data and the objective sleep time data; performing a classification of the subject to one of multiple sleep perception classes based on the sleep time misperception; and generating an output signal representative of a suggested action based on the classification. The suggested action relates to providing sleep-related therapy to the subject.

A system for producing spatial audio from sonification of electrocardiogram (ECG) signals includes a sound player and a controller. The controller has a memory that stores instructions and a processor that executes the instructions. When executed by the processor, the instructions cause the system to: obtain ECG data; map the ECG data to sound as mapped ECG data; sonify the mapped ECG data; generate spatial audio based on sonifying the mapped ECG data by converting the mapped ECG data to spatial audio; and play the sound as spatial audio from the sound player.

A computer-implemented method (10) of medical image analysis for assessment of Coronary Artery Disease (CAD). The method includes receiving medical image data (62) and second data (52) related to a patient, where the second data is clinical data which is not image data. A pre-defined CAD analysis operation (16) is performed, which includes retrieving a pre-defined medical image analysis algorithm (60) configured to generate at least one stenosis severity score (66). The method generates one or more CAD findings (82) based on the medical image analysis algorithm, the received medical image data, and the received second data.

Proposed concepts thus aim to provide schemes, solutions concepts, designs, methods, and systems pertaining to an oral health care device containing a driven treatment component and a first passage within which gas and/or liquids can flow. In particular, embodiments provide a valve for controlling the flow of gases and/or liquids within the passage in a first direction and a biasing system for biasing the valve such that when the driving means is not activated the valve is in a first position which prohibits the flow of fluids in the passage in the first direction, and when the driving means is activated the valve is in a second position which permits the flow of fluids in the passage in the first direction.

A system (CS) and related method for computing imagery. The system (CS) may comprise an input interface (IN) for receiving input data including an initial image obtainable by an imaging modality (IA1) of a region of interest, ROI, prior to a procedure performable in relation to the ROI. A predictor component (PC) computes a predicted virtual image representative of the ROI post-procedure. An output interface (OUT) provides the predicted image.

A personal care system comprising a personal care appliance with one or more functional components, a tracking subsystem configured to determine a current usage location of the personal care appliance, and a control subsystem. The control subsystem includes one or more processors configured to compare the current usage location to a predetermined guidance sequence and control a functionality of at least one functional component of the personal care appliance in dependence upon the comparison. Controlling the functionality comprises changing a power consumption of the at least one functional component of the personal care appliance. The system enables adaptive power management based on usage location adherence to a guidance sequence.

Disclosed herein is a method of X-ray imaging. The method comprises receiving (200) a chest X-ray image (124, 124', 124"). The method further comprises registering (202) a pulmonary blood density mask (122) to the chest X-ray image. The method further comprises generating (204) an enhanced chest X-ray image (132, 132') by compensating for a spatially dependent blood density (602, 604, 700, 702, 704) in the chest X-ray image using the registered pulmonary blood density mask (128). The method comprises further providing (206) the enhanced chest X-ray image.

A method predicts a health condition based on a gas or gas mixture in an oral cavity by capturing gas sample data during a personal oral care session using a sensor system integrated into a personal oral care device. The captured data is then analyzed to determine a change pattern. A machine-learning model is applied to the change pattern to generate a health condition prediction output, providing a prediction of the health condition.

The present disclosure provides concepts for predicting wear of a brush head of an oral care device. The brush head comprises a brush body and cleaning elements mounted on the brush body for contact with an oral surface of a user. An image of the brush head including the cleaning elements is obtained. The image is then compared to reference image data describing an expected appearance of the cleaning elements of an unused brush head. A wear score indicating a degree of wear of the brush head is generated based on a result of the comparison of the image and the reference image data. In this way, an accurate prediction of an extent to which a brush head is worn may be obtained, based on which appropriate action can be recommended and/or actioned.

There is provided a computer-implemented method for sleep staging of a subject, comprising: receiving a first measurement data representative of a first physiological property relating to the autonomic nervous system of the subject; providing a first sleep stage classification by using a first sleep stage classifier to classify sleep stages of the subject based on the first measurement data; providing a second sleep stage classification by using a second sleep stage classifier, wherein the second sleep stage classifier is different than the first sleep stage classifier; determining a difference between a first performance of the first sleep stage classifier and a second performance of the second sleep stage classifier; determining, based on the difference, an autonomic modulation value representative of an autonomic modulation of the subject, wherein the autonomic modulation represents a relationship between the autonomic nervous system and a central nervous system of the subject.