A plurality of electrophysiological signals measured by a respective plurality of electrodes carried by a multi-dimensional catheter can be sorted relative to a direction of interest, such as a cardiac activation wavefront direction. An electroanatomical mapping system can be used to determine the orientation of the multi-dimensional catheter relative to the direction of interest. For example, the user can manually adjust the orientation by manipulating a slider, a wheel, or a similar graphical user interface control. As another example, the user can draw a presumed orientation on a geometric model. Once the orientation is determined, the system can sort the plurality of electrophysiological signals and output a graphical representation of the sorted plurality of electrophysiological signals, for example as a plurality of traces.

A system and method for atrial fibrillation detection in non-noise ECG data with the aid of a digital computer are provided. Electrocardiography (ECG) features and annotated patterns of the features are maintained in a database, at least some of the patterns associated with atrial fibrillation. A classifier is trained based on the annotated patterns, the classifier implemented by a convolutional neural network. A representation of an ECG signal recorded by one or more ambulatory monitors is received. Noise is detected in the representations and ECG features in the representation falling within each of the non-noise temporal windows are detected. The trained classifier is used to identify patterns of the ECG features. A value indicative of whether portions of the representation are associated the patient experiencing atrial fibrillation is calculated. That one or more of the portions are associated with the patient experiencing atrial fibrillation is determined.

Automatically determining a medical recommendation for a patient based on multiple medical images from multiple different medical imaging modalities. In some embodiments, a method may include receiving a first and second medical images of a patient from first and second medical imaging modalities, mapping a first region of interest (ROI) on the first medical image to a second ROI on the second medical image, generating first annotation data related to the first ROI and second annotation data related to the second ROI, generating first medical clinical data related to the first ROI and second medical clinical data related to the second ROI, inputting, into a machine learning classifier, the first and second annotation data and the first and second medical clinical data, and automatically determining, by the machine learning classifier, a medical recommendation for the patient related to a medical condition of the patient.

The present embodiments provide systems and methods for, among others, tracking sensor insertion locations in a continuous analyte monitoring system. Data gathered from sensor sessions can be used in different ways, such as providing a user with a suggested rotation of insertion locations, correlating data from a given sensor session with sensor accuracy and/or sensor session length, and providing a user with a suggested next insertion location based upon past sensor accuracy and/or sensor session length at that location.

The present invention relates to the field of medical monitoring, and in particular non-contact, video-based monitoring of pulse rate, respiration rate, motion, and oxygen saturation. Systems and methods are described for capturing images of a patient, producing intensity signals from the images, filtering those signals to focus on a physiologic component, and measuring a vital sign from the filtered signals.

An initial set of parameters for operating one or more detection tools is automatically derived and subsequently adjusted so that each detection tool is more or less sensitive to signal characteristics in a region of interest. Detection tool(s) may be applied to physiological signals sensed from a patient (such as EEG signals) and may be configured to run in an implanted medical device that is programmable with the parameters to look for rhythmic activity, spiking, and power changes in the sensed signals, etc. A detection tool may be selected and parameter values derived in a logical sequence and/or in pairs based on a graphical representation of an activity type which may be selected by a user, for example, by clicking and dragging on the graphic via a GUI. Displayed simulations allow a user to assess what will be detected with a derived parameter set and then to adjust the sensitivity of the set or start over as desired.

Intravascular devices, systems, and methods are disclosed. In some embodiments, a medical imaging system for imaging vasculature of a patient is provided. The imaging system includes a console that has one or more processors with a medical imaging system interface running thereon, an acquisition card in communication with the one or more processors and in communication with a patient interface module (PIM), and an intravascular imaging component in communication with the PIM and disposed on a distal end of a flexible elongate member. The medical imaging system interface provides a plurality of settings groups for selection by a user, each of the settings groups having pre-acquisition parameters and post-acquisition parameters that are optimal for imaging a desired viewing target within the vasculature. Associated methods and computer-readable media are provided.

A contactless health monitoring device may perform a beam steering process that creates a plurality of beam-steered radar data streams from the received radar data stream. The contactless health monitoring device may determine breathing displacement for a user in relation to time for each spatial zone radar data stream. The contactless health monitoring device may analyze the breathing displacement for the user in relation to time for each spatial zone radar data stream. The contactless health monitoring device may output a screening result based on analyzing the breathing displacement for the user.

A system for measuring blood oxygenation levels and capillary refill time (CRT) of a user includes a camera configured to capture a video stream that includes a series of images representing a process of first squeezing a fingernail of the user to a blanched state, and then releasing to a resting state, and a Convolutional Neural Network (CNN) based processing unit configured to process an input image to create a two-dimensional representation of features, remove spatial relationships in the two-dimensional representation, generate non-spatial metadata, identify the fingernail in the resting state, and regions within a static image of the fingernail, and configured to generate a blood oxygenation value, and a measurement confidence level, and generate a CRT value of the user by applying the blood oxygenation value, and the measurement confidence level to a series of images captured by the camera over time.

Systems and methods are disclosed to determine one or more sensing zones on a body surface for electrocardiographic mapping of a region of interest associated with the heart. The sensing zone can be utilized to facilitate acquisition, processing and mapping of electrical activity for the corresponding region of interest. In other examples, an application-specific arrangement of electrodes can also be provided based on the sensing zone that is determined for the region of interest.