Devices, systems, and techniques are disclosed for managing electrical stimulation therapy and/or sensing of physiological signals such as brain signals. For example, a system may assist a clinician in identifying one or more electrode combinations for sensing a brain signal. In another example, a user interface may display brain signal information and values of a stimulation parameter at least partially defining electrical stimulation delivered to a patient when the brain signal information was sensed.

Devices, systems, and techniques are disclosed for managing electrical stimulation therapy and/or sensing of physiological signals such as brain signals. For example, a system may assist a clinician in identifying one or more electrode combinations for sensing a brain signal. In another example, a user interface may display brain signal information and values of a stimulation parameter at least partially defining electrical stimulation delivered to a patient when the brain signal information was sensed.

An apparatus for classifying mental stress includes a sequence folding layer configured to convert a sequence image of an electrocardiogram signal into an image in an array form; a CNN layer configured to generate a feature map by performing a convolution operation on the image in an array form; a sequence unfolding layer configured to convert the generated feature map into a sequence image; a flatten layer configured to convert the converted sequence image into one-dimensional data; and a long-short term memory network layer configured to extract feature values using a weighted value on the converted one-dimensional data; and a classification module configured to classify stress according to the extracted feature values.

An apparatus for classifying mental stress includes a sequence folding layer configured to convert a sequence image of an electrocardiogram signal into an image in an array form; a CNN layer configured to generate a feature map by performing a convolution operation on the image in an array form; a sequence unfolding layer configured to convert the generated feature map into a sequence image; a flatten layer configured to convert the converted sequence image into one-dimensional data; and a long-short term memory network layer configured to extract feature values using a weighted value on the converted one-dimensional data; and a classification module configured to classify stress according to the extracted feature values.

Among other things, techniques are described for screening and monitoring the health of a vehicle user including receiving sensor data produced by a sensor at the vehicle, processing the sensor data to determine at least one health condition of the user of the vehicle, and in response to determining the at least one health condition, executing a vehicle function selected from a plurality of vehicle functions based on the at least one health condition.

The present disclosure generally relates to user interfaces for health monitoring. Exemplary user interfaces for initial setup of health monitoring using a first electronic device and a second electronic device is described. Exemplary user interfaces for recording biometric information for use in health monitoring is described. Exemplary user interfaces for using an input device while recording biometric information for health monitoring is described. Exemplary user interfaces for viewing and managing aspects of health monitoring is described.

A method includes: obtaining an electrocardiosignal of a target user; importing the electrocardiosignal into a preset atrial fibrillation signal classification model, to obtain a signal type, of the electrocardiosignal, output by the atrial fibrillation signal classification model, where the atrial fibrillation signal classification model is obtained through training with an atrial fibrillation patient being a model training sample; and calculating, based on the signal type of the electrocardiosignal, a risk level of an atrial fibrillation occurrence, to predict whether the target user is to have an atrial fibrillation attack.

A method includes: obtaining an electrocardiosignal of a target user; importing the electrocardiosignal into a preset atrial fibrillation signal classification model, to obtain a signal type, of the electrocardiosignal, output by the atrial fibrillation signal classification model, where the atrial fibrillation signal classification model is obtained through training with an atrial fibrillation patient being a model training sample; and calculating, based on the signal type of the electrocardiosignal, a risk level of an atrial fibrillation occurrence, to predict whether the target user is to have an atrial fibrillation attack.

A diagnosis report generation method and terminal device are provided. The method includes obtaining electrocardiography ECG data, determining that the ECG data includes abnormal heartbeat data, obtaining, based on the ECG data, an abnormal heartbeat waveform corresponding to the abnormal heartbeat data, combining a normal heartbeat waveform and the abnormal heartbeat waveform to obtain an abnormality comparison image, and generating a diagnosis report based on the abnormality comparison image. The abnormal heartbeat waveform is obtained by extracting and analyzing the ECG data, so that the abnormal heartbeat waveform can be combined and compared with the normal heartbeat waveform, to obtain the abnormality comparison image for generating the diagnosis report.

The present invention relates to a method to provide a mean temporal spatial isochrone (TSI) path relating to an ECG feature (wave form) of interest, such as the activation of the heart from a single point (QRS), relative to the heart in a torso while using an ECG measurement from an ECG recording device. The method includes: receiving ECG measuring data from the ECG recording device; determining vector cardiogram (VCG) data; receiving a model of the heart, preferably with torso, as an input, preferably based on a request including request parameters; determining mean TSI data values representing the TSI path relating to an electrophysiological phase representing the ECG feature, the mean TSI providing a location within the heart representing the mean location of the ECG feature at the corresponding time; positioning the mean TSI path and preferably the vector cardiogram data points in the model of the heart and/or torso at an initial position; and rendering the model of the heart, preferably with torso, with the mean TSI path, preferably with VCG data related to the TSI, for displaying on a display screen for interpretation of the displayed rendering.