Abstract: A computer-implemented method for targeted monitoring of a patient in a bed comprises: receiving an image of the bed; predicting, based on a posture of the patient in the image, a pressure score for one or more contact regions between the patient and the bed; and providing an indication of the pressure score for the one or more contact regions.

Abstract: A system for monitoring autoregulation may include processing circuitry configured to receive a blood pressure signal indicative of an acquisition blood pressure of a patient at an acquisition site and an oxygen saturation signal indicative of an oxygen saturation of the patient. The processing circuitry may determine a cerebral autoregulation status value based on the blood pressure signal and the oxygen saturation signal. The processing circuitry may determine a non-cerebral autoregulation status value based on the cerebral autoregulation status value and an adjustment value. The processing circuitry provide to an output device a signal indicative of the non-cerebral autoregulation status value and a signal indicative of the cerebral autoregulation status value to enable a clinician to monitor the autoregulation status of the patient.

Abstract: A method and system for performing a depth data processing procedure for obtaining physiological information from depth data. The depth processing procedure comprises obtaining depth data representing depth across a field of view, and deriving at least one signal from said depth data. The method further comprises: obtaining further information from at least one of: the depth data and the at least one signal derived from the depth data; using at least said further information to determine an absence of respiratory motion in the field of view; and setting a flag for the depth data processing procedure or a further procedure based on said determination of the absence of respiratory motion in the field of view.

Abstract: A method including determining, based on an image signal received from a camera focused on at least a portion of a patient, a breathing signal, determining an onset of a desaturation event for the patient and an onset of a bradycardia event for the patient, determining length of a bradycardia-desaturation (BD) period between the onset of the desaturation event for the patient and the onset of the bradycardia event for the patient, in response to determining that the length of the BD period is less than a BD time threshold, determining whether a change in of breathing of the patient within an apnea search period indicates an apnea event, and in response to determining that the change in of breathing of the patient within the apnea search period indicates an apnea event, displaying an occurrence of an apnea-bradycardia-desaturation event.

Abstract: In some examples, a system is configured to determine a non-cerebral autoregulation status value of a patient using machine learning. In some examples, processing circuitry of the system is configured to determine, using a neural network algorithm that has been trained via machine learning training, an individualized adjustment value that is individualized for the patient, including inputting physiological data associated with the patient. The processing circuitry may determine a non-cerebral autoregulation status of the patient based on a cerebral autoregulation value of the patient based on the non-cerebral autoregulation status value of the patient and the adjustment value.

Abstract: The present invention relates to the field of medical monitoring, and in particular non-contact monitoring of one or more physiological parameters in a region of a patient during surgery. Systems, methods, and computer readable media are described for generating a pulsation field and/or a pulsation strength field of a region of interest (ROI) in a patient across a field of view of an image capture device, such as a video camera. The pulsation field and/or the pulsation strength field can be generated from changes in light intensities and/or colors of pixels in a video sequence captured by the image capture device. The pulsation field and/or the pulsation strength field can be combined with indocyanine green (ICG) information regarding ICG dye injected into the patient to identify sites where blood flow has decreased and/or ceased and that are at risk of hypoxia.

Abstract: 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.

Abstract: Vision-based stimulus monitoring and response systems and methods are presented, wherein detection, via image(s) of a patient through an external stimulus, such as a caregiver, prompts analysis of the response of the patient, via secondary patient sensors or via analysis of patient image(s), to determine an autonomic nervous system (ANS) state.

Abstract: An autoregulation monitoring device may display, on a graph on the display screen, a blood pressure signal of a patient; indicate, on the graph, an autoregulation threshold that separates an intact autoregulation area of the graph and an impaired autoregulation area of the graph. The device may indicate, on the graph, a plurality of threshold offsets that are each separated from the autoregulation threshold, wherein each one of the plurality of threshold offsets is selectable by a user. The device may receive an indication of user input that corresponds to selection of a threshold offset from the plurality of threshold offsets and may update the autoregulation threshold on the graph based on the selected threshold offset.

Abstract: The present invention relates to the field of medical monitoring, and, in particular, to non-contact detecting and monitoring of patient breathing. Systems, methods, and computer readable media are described for calculating a change in depth of a region of interest (ROI) on a patient and assigning one or more visual indicators to at least a portion of a graphic based on the calculated changes in depth and/or based on a tidal volume signal generated for the patient. In some embodiments, the systems, methods, and/or computer readable media can display the visual indicators overlaid onto at least the portion in real-time and/or can display the tidal volume signal in real-time. The systems, methods, and/or computer readable media can trigger an alert and/or an alarm when a breathing abnormality is detected.

Abstract: Methods for improving the accurate determination of respiratory rate, the respiratory rate being measured or monitored from depth measurements from a non-contact monitoring system. The methods include analyzing a waveform obtained from a respiratory parameter and discarding data that does not meet criteria. The respiratory rate may be obtained from an original waveform, a processed waveform, or from a power spectrum derived from the original or processed waveform.

Abstract: A system for monitoring autoregulation includes an oxygen saturation sensor configured to obtain an oxygen saturation signal indicative of an oxygen saturation of a patient. The system also includes a controller having a processor configured to receive a blood pressure signal indicative of a blood pressure of the patient and the oxygen saturation signal, determine a change in the oxygen saturation signal and a change in the blood pressure signal over a period of time, and provide an indication that the patient's autoregulation is intact if the oxygen saturation changes by more than an oxygen saturation threshold and if the blood pressure changes by less than a blood pressure threshold during the period of time.

Abstract: A patient monitoring system may include processing circuitry configured to receive an indication of a surgical event, obtain a nociception parameter of a patient, compare the nociception parameter of the patient to a nociception threshold to detect a nociception event, determine whether the surgical event corresponds to the nociception event, and in response to determinizing whether the surgical event corresponds to the nociception event, provide an indication to adjust an amount of analgesic administered to the patient.

Abstract: Implementations described herein discloses, a method of AI based video tagging for alarm management includes receiving, using a processor, a video stream, the video stream comprising a sequence of images for at least a portion of a patient, determining, using the processor, a physiological parameter for the patient based on the sequence of images, detecting, using machine learning, presence of a noise object and setting a interaction-flag to a positive value in response to detecting the noise object, comparing a quality level of the sequence of images with a threshold quality level, and modifying an alarm level based on the value of the interaction-flag and comparison of the quality level of the sequence of depth images with the threshold quality level.

Abstract: According to an aspect, a method for determining an augmented vital sign, such as heart rate or heart rate variability, includes obtaining signals from a patient when the patient is both active and inactive. An activity signal is generated from the first signal, and a physiological signal is generated from the second signal. An activity weight factor is calculated from the activity signal, and a vital sign signal is calculated from the physiological signal. A mapping from the activity signal to the vital sign aids the determination of the vital sign through a motion period. The augmented vital sign is a result of the combination of first and second values, including the product of the activity weight factor and an activity derived vital sign, and the product of the vital sign signal and the activity weight factor less than one.

Abstract: Implementations described herein disclose a method of monitoring motion of a patient, the method includes receiving, using a processor, a video stream, the video stream comprising a sequence of images for at least a portion of a patient, dividing the video stream into a plurality of temporal video sequences, each of the temporal video sequences having a plurality of frames each being apart from each other, generating a matrix of depth difference frames, and determining a machine learning (ML) input feature matrix based on the matrix of depth difference frames.

Abstract: Use of non-contact monitoring systems to monitor a subject (e.g., neonate, baby, infant, child) to determine various physiological characteristics of the subject. Physical characteristic that can be monitored, measured, and/or determined include length, volume, weight, BMI, and head circumference. Other characteristics such as reactions to stimulus, development of motor skills, smiling, and even development of language can also be monitored.

Abstract: A system for monitoring autoregulation includes an oxygen saturation sensor configured to obtain an oxygen saturation signal indicative of an oxygen saturation of a patient. The system also includes a controller having a processor configured to receive a blood pressure signal indicative of a blood pressure of the patient and the oxygen saturation signal, determine a change in the oxygen saturation signal and a change in the blood pressure signal over a period of time, and provide an indication that the patient's autoregulation is intact if the oxygen saturation changes by more than an oxygen saturation threshold and if the blood pressure changes by less than a blood pressure threshold during the period of time.

Abstract: The present invention relates to the field of medical monitoring, and, in particular, to non-contact detecting and monitoring of patient breathing. Systems, methods, and computer readable media are described for calculating a change in depth of a region of interest (ROI) on a patient and assigning one or more visual indicators to at least a portion of a graphic based on the calculated changes in depth and/or based on a tidal volume signal generated for the patient. In some embodiments, the systems, methods, and/or computer readable media can display the visual indicators overlaid onto at least the portion in real-time and/or can display the tidal volume signal in real-time. The systems, methods, and/or computer readable media can trigger an alert and/or an alarm when a breathing abnormality is detected.

Abstract: In some examples, a computing system tracks, across a plurality of predictions, prediction performance of a first oxygen saturation prediction model used by one or more patient monitoring devices by comparing a respective prediction made by the first oxygen saturation prediction model to a corresponding ground truth. The computing system determines whether the prediction performance of the first oxygen saturation prediction model meets a performance metric, wherein the performance metric includes an accuracy level, a specificity level, a sensitivity level, or any combination thereof. The computing system may, in response to determining that the prediction performance of the first oxygen saturation prediction model does not meet the performance metric, cause the one or more patient monitoring devices to switch to a second oxygen saturation prediction model to predict future oxygen saturation levels of one or more patients.