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: The present invention relates to the field of medical monitoring, and in particular non-contact monitoring and communication with other medical monitoring devices. Systems and methods are described for receiving a video signal of a medical monitoring device that is outputting a light signal, identifying the light signal emitted by the medical monitoring device from the video signal, decoding information from the light signal, and determining a communication from the decoded information related to a patient being monitored or the medical monitoring device itself.

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: Use of non-contact patient monitoring systems to detect, diagnose, monitor and/or adjust therapy for diseases or conditions that have motion-based symptoms, symptoms such as limb tremors, spasms, etc. that can be correlated to diseases or conditions such as Essential tremor, Parkinson's disease, restless leg syndrome, or a diabetic episode. Information garnered by the non-contact monitoring system, information such as location, frequency, severity, and duration of the motion, can be collected and analyzed so that a diagnosis can be made and/or a treatment plan developed and/or adjusted by the patient's caretaker. The non-contact monitoring can provide real-time feedback regarding effectiveness of therapies or treatments, such as, e.g., neurological stimulation.

Abstract: The present technology 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. In some embodiments, the systems, methods, and/or computer readable media can identify steep changes in depths. For example, the systems, methods, and/or computer readable media can identify large, inaccurate changes in depths that can occur at edge regions of a patient. In these and other embodiments, the systems, methods, and/or computer readable media can adjust the identified steep changes in depth before determining one or more patient respiratory parameters.

Abstract: A patient monitor for monitoring cerebral activity of a patient may include a processor configured to determine a depth of consciousness index for the patient based on electroencephalography (EEG) data and determine regional oxygen saturation for the patient based on regional oximetry data. Additionally, the processor may be configured to determine a metric associated with cerebral activity of the patient based at least in part on the one or more values of the depth of consciousness index and the one or more regional oxygen saturation values and to provide the one or more values of a depth of consciousness index and the metric to an output device.

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: 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: 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: 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.

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: A system may generate a waveform signal indicative of a biometric parameter of a target subject based on motion data associated with the target subject. The motion data may be provided by a depth sensing device and be substantially free of image data. The system may set a threshold condition based on one or more characteristics of the waveform signal. The system may output a control signal to a stimulation device based on the waveform signal satisfying the threshold condition.