Abstract: An example system includes a camera system, memory, and processing circuitry coupled to the camera system and memory. The processing circuitry is configured to obtain image data and determine a visualization mask based on pixels associated with respiration. The processing circuitry is configured to determine a bounding box based on the visualization mask. The processing circuitry is configured to determine a sampling region for the image data based on an average including the bounding box and at least one previously determined bounding box. The processing circuitry is configured to determine at least a portion of a volume waveform based at least in part on the sampling region, determine at least one respiration parameter based on the volume waveform, and output, for display, the at least one respiration parameter.

Abstract: Use of non-contact monitoring systems to monitor a subject (e.g., baby, infant, child) in a sleeping or resting environment. The systems identify the position of the subject and alert a caregiver if the subject is in a potentially hazardous position, such as lying prone (on their front) or if the subject has remained in the same position for a period of time longer than a threshold time. The non-contact monitoring systems can be configured to detect a physical attribute (e.g., respiration rate, tidal volume, pulse, etc.).

Abstract: Use of non-contact monitoring systems to monitor a subject (e.g., baby, infant, child) in a sleeping or resting environment. The systems utilize artificial intelligence (AI) to identify potential hazards to the subject and alert a caregiver. The systems can be trained to recognize the location of an object as a potential hazard, recognize the location of an object as not a potential hazard, and/or to recognize the type of object as not a potential hazard. The caregiver may acknowledge the potential hazard is indeed a hazard or may clear the potential hazard and the alert, which the system will remember for subsequent like occurrences.

Abstract: In some examples, a device includes a memory configured to store a first relationship between a blood pressure and another physiological parameter of a patient, the first relationship being indicative of cerebral autoregulation of the patient. The device also includes processing circuitry configured to receive first and second signals indicative of the blood pressure and the physiological parameter, respectively, of a patient. The processing circuitry is also configured to determine an expected value and an actual value of the physiological parameter at a particular blood pressure value of the first physiological signal. The processing circuitry is configured to determine, based on a difference between the actual value and the expected value, and store, in the memory, a second relationship between the blood pressure and the physiological parameter, the second relationship being indicative of a change in the cerebral autoregulation of the patient from the first relationship.

Abstract: In some examples, a system is configured to determine, using a neural network algorithm of a cerebral autoregulation model, a cerebral autoregulation status of the patient based at least in part on a blood pressure of the patient over a period of time and regional cerebral oxygen saturation of the patient over the period of time.

Abstract: In some examples, a method includes receiving a signal indicative of a blood pressure of a patient and identifying at least one first portion of the signal comprising a first characteristic of the signal exceeding a first threshold. The method also includes identifying at least one first portion of the signal comprising a second characteristic of the signal exceeding a second threshold, the first characteristic being different than the second characteristic. The method further includes determining a filtered signal indicative of the blood pressure of the patient by excluding the at least one first portion and the at least one second portion from the signal. The method includes determining a set of mean arterial pressure values based on the filtered signal and determining an autoregulation status of the patient based on the set of mean arterial pressure values.

Abstract: Implementations described herein disclose a method of classifying oxygen level desaturation events. In one implementation, the method includes receiving input signal sequences, the input signals indicative of a physiological condition of a patient, generating an input sequence of oxygen saturation levels based on the input signal sequence, comparing the input sequence of oxygen saturation levels to a desaturation alarm threshold to determine a desaturation event, generating an input feature matrix based on at least one of the input signal sequences and the input sequence of oxygen saturation levels, and classifying based on the input feature matrix, using a neural network, the desaturation event being a severe desaturation event (SDE) or a non-severe desaturation event (non-SDE).

Abstract: The disclosed technology provides image-based patient monitoring by capturing a first image, by an image capture sensor, of a predefined region of a patient at a first time, capturing a second image, by the image capture sensor, of the predefined region of the patient at a second time, determining whether the first captured image and the second captured image satisfy a non-respiratory motion condition, the non-respiratory motion condition based in part on a predefined relationship between captured images and motion of a patient attributable to patient actions other than respiration of the patient, classifying a motion of the patient as non-respiratory motion when the non-respiratory motion condition is satisfied, and transmitting an instruction to display an indication of veracity of a respiratory measurement of the patient measured between the first time and the second time based on the classified motion.

Abstract: In some examples, a system is configured to determine, using a neural network algorithm of a cerebral autoregulation model, a cerebral autoregulation status of the patient based at least in part on a blood pressure of the patient over a period of time and regional cerebral oxygen saturation of the patient over the period of time.

Abstract: In some examples, a patient monitoring system includes processing circuitry configured to detect an occurrence of a nociception event of a patient during a medical procedure. The processing circuitry may, for example, monitor a nociception parameter of the patient during the medical procedure, determine a characteristic nociception parameter at a point in time based at least in part on values of the nociception parameter over a period of time, and determine, based at least in part on comparing the characteristic nociception parameter at the point in time with a nociception threshold, a nociception event has occurred at the point in time. In some examples, the processing circuitry is configured to provide an indication to adjust an amount of analgesic administered to the patient based on the determination that the nociception event has occurred at the point in time.

Abstract: In some examples, a patient monitoring system includes processing circuitry configured to detect an occurrence of a nociception event of a patient during a medical procedure. The processing circuitry may, for example, determine, based at least in part on a nociception parameter of the patient in an interrogation window, a nociception parameter level, determine, based at least in part on the nociception parameter of the patient in a baseline window that corresponds to the interrogation window, a baseline nociception parameter level, determine a difference in nociception parameter levels between the baseline nociception parameter level and the nociception parameter level, detect, based at least in part on the difference in nociception parameter levels, an occurrence of a nociception event, and in response to detecting the occurrence of the nociception event, provide an indication to adjust an amount of analgesic administered to the patient.

Abstract: Systems and methods for non-contact monitoring are disclosed herein. An example system includes at least one depth determining device configured to determine depth data representing depth across a field of view; a processor configured to process the depth data to obtain time varying depth or physiological information associated with respiration and/or another physiological function; and a projector configured to project one or more images into the field of view, wherein at least part of the one or more images is based on the obtained time varying depth or physiological information.

Abstract: A patient monitoring system may include processing circuitry configured to receive an indication of a medical event, determine a set of nociception parameters of a patient that corresponds to the medical event, determine a nociception threshold based at least in part on the set of nociception parameters of the patient, compare a nociception parameter of the patient to the nociception threshold to detect a nociception event, and in response to detecting the nociception event, providing an indication to adjust an amount of analgesic administered to the patient.

Abstract: In some examples, a device includes processing circuitry configured to determine a set of correlation coefficient values for first and second physiological parameters. The processing circuitry is further configured to determine a metric of the correlation coefficient values for a first plurality of bins and for a second plurality of bins, wherein each bin of the first plurality has a first bin parameter and each bin of the second plurality of bins has a second bin parameter different than the first bin parameter. The processing circuitry is also configured to determine a composite estimate of a limit of autoregulation of the patient based on the metric for the first plurality of bins and the metric for the second plurality of bins. The processing circuitry is configured to determine an autoregulation status based on the composite estimate and output, for display via the display, an indication of the autoregulation status.

Abstract: A method for monitoring autoregulation includes, using a processor, using a processor to execute one or more routines on a memory. The one or more routines include receiving one or more physiological signals from a patient, determining a correlation-based measure indicative of the patient's autoregulation based on the one or more physiological signals, and generating an autoregulation profile of the patient based on autoregulation index values of the correlation-based measure. The autoregulation profile includes the autoregulation index values sorted into bins corresponding to different blood pressure ranges. The one or more routines also include designating a blood pressure range encompassing one or more of the bins as a blood pressure safe zone indicative of intact regulation and providing a signal to a display to display the autoregulation profile and a first indicator of the blood pressure safe zone.

Abstract: In some examples, a system includes processing circuitry configured to determine that an oxygen saturation level of a patient is at or below a desaturation threshold, and, in response, determine whether the oxygen saturation level is at or below the desaturation threshold at the end of a calculation period. The processing circuitry may, in response to determining that the oxygen saturation level of the patient is at or below the desaturation threshold at the end of the calculation period, predict, using an oxygen saturation prediction model, whether the oxygen saturation level of the patient will increase above the desaturation threshold by the end of a predefined time period. In response to predicting that the oxygen saturation level of the patient will increase above the desaturation threshold by the end of the predefined time period, the processing circuitry refrains from outputting an indication of the patient experiencing an oxygen desaturation event.

Abstract: System and methods for non-contact monitoring in vehicles are described. The systems and methods may use depth sensing cameras as part of the non-contact monitoring system. In some embodiments, depth data from at least one depth sensing device that has a field of view of at least part of the interior of the vehicle is received, wherein the depth data represents depth information as a function of position across the field of view. The depth data is then processed to obtain further information related to the occupant within the vehicle.

Abstract: A thermal camera system is used to derive cardiac and respiratory signals or information via extraction of cardiac and/or respiratory signals from a thermal signal at a region of interest (ROI), by a peak picking analysis of the thermal signal, or via analysis of a wavelet transform of the thermal signal.

Abstract: A method for monitoring autoregulation includes, using a processor, using a processor to execute one or more routines on a memory. The one or more routines include receiving one or more physiological signals from a patient, determining a correlation-based measure indicative of the patient's autoregulation based on the one or more physiological signals, and generating an autoregulation profile of the patient based on autoregulation index values of the correlation-based measure. The autoregulation profile includes the autoregulation index values sorted into bins corresponding to different blood pressure ranges. The one or more routines also include designating a blood pressure range encompassing one or more of the bins as a blood pressure safe zone indicative of intact regulation and providing a signal to a display to display the autoregulation profile and a first indicator of the blood pressure safe zone.

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.