Generally, the present disclosure is directed to systems and methods for measuring heart rate and respiratory rate using a camera such as, for example, a smartphone camera or other consumer-grade camera. Specifically, the present disclosure presents and validates two algorithms that make use of smartphone cameras (or the like) for measuring heart rate (HR) and respiratory rate (RR) for consumer wellness use. As an example, HR can be measured by placing the finger of a subject over the rear-facing camera. As another example, RR can be measured via a video of the subject sitting still in front of the front-facing camera.

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.

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.

To obtain a predictive model that shows a diagnostic prediction result with higher accuracy and high medical validity. A medical imaging apparatus includes an imaging unit that collects an image signal of an inspection target, and an image processing unit that generates first image data from the image signal and performs image processing of the first image data. The image processing unit includes a feature quantity extraction unit that extracts a first feature quantity from the first image data, a feature quantity abstraction unit that abstracts the first feature quantity to extract a second feature quantity, a feature quantity conversion unit that converts the second feature quantity into a third feature quantity extracted by second image data, and an identification unit that uses the converted third feature quantity to calculate a predetermined parameter value.

The present invention relates to the field of medical monitoring, and in particular non-contact video monitoring to measure tidal volume of a patient. Systems, methods, and computer readable media are described for determining a region of interest of a patient and monitoring that region of interest to determine tidal volume of the patient. This may be accomplished using a depth sensing camera to monitor a patient and determine how their chest and/or other body parts are moving as the patient breathes. This sensing of movement can be used to determine the tidal volume measurement.

A device and method for detecting cerebral microbleeds use magnetic resonance images. The disclosed device includes a preprocessing unit that normalizes an SWI image and a phase image, respectively, of the magnetic resonance images, and performs phase image conversion for inverting a code of the normalized phase image, a YOLO neural network module that receives a two-channel image in which the preprocessed SWI image and phase image are concatenated and detects a plurality of candidate regions for the cerebral microbleeds, and a cerebral microbleeds determination neural network module that receives patch images of candidate regions of the SWI image and phase image based on the candidate regions and determines whether the patch images of each candidate region are an image with a symptom of the cerebral microbleeds through a neural network operation.

The present disclosure relates to a method and a system for detecting a neurological disease and an eye gaze-pattern abnormality related to the neurological disease of a user. The method comprises displaying stimulus videos on a screen of an electronic device and simultaneously filming with a camera of the electronic device to generate a video of the user's face for each one of the stimulus videos, each one of the stimulus videos corresponding to a task. The method further comprises providing a machine learning model for gaze predictions, generating the gaze predictions for each video frame of the recorded video, and determining features for each task to detect the neurological disease using a pre-trained machine learning model.

A medical imaging system including a microwave antenna array having a transmitting antenna and a plurality of receiving antennae, wherein the transmitting antenna is configured to transmit microwave signals so as to illuminate a body part of a patient and the receiving antennae are configured to receive the microwave signals following scattering within the body part; a processor configured to process the scattered microwave signals and generate an output indicative of the internal structure of the body part to identify a region of interest within the body part; and a biopsy device comprising a biopsy needle movable relative to the microwave antenna array; wherein the receiving antennae are further configured to receive microwave signals scattered or emitted by the biopsy needle and the processor is further configured to monitor a position of the biopsy needle and to guide the biopsy needle to the identified region of interest within the body part.

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. Examples include flood fill methods and skin tone filtering methods.

The present disclosure relates to the field of emotion recognition technologies, and specifically, to a visual perception-based emotion recognition method. The method in the present disclosure includes the following steps: step 1: acquiring a face video by using a camera, performing face detection, and locating a region of interest (ROI); step 2: extracting color channel information of the ROI for factorization and dimension reduction; step 3: denoising and stabilizing signals and extracting physiological parameters including heart rate and breathing waveforms, and heart rate variability (HRV); step 4: extracting pre-training nonlinear features of the physiological parameters and performing modeling and regression of a twin network-based support vector machine (SVM) model; and step 5: reading a valence-arousal-dominance (VAD) model by using regressed three-dimensional information to obtain a specific emotional representation.