Abstract: Methods for enhancing the image of a subject, such as a patient, in a video non-contact monitoring system to provide an enhanced image with clear distinction of the subject from the background. The methods include applying a histogram equalization transform, such as a contrast limited adaptive histogram equalization (CLAHE) transform, to the depth data obtained from a camera of the monitoring system. In some embodiments, the enhanced image of the subject is merged with an overlay image of a monitored physiological parameter determined by the non-contact patient monitoring system.

Abstract: An example device for determining heart rate variability (HRV) includes a memory configured to store a sensed pulse rate signal indicative of one or more sensed pulse rates and processor circuitry. The processor circuitry is configured to receive the sensed pulse rate signal and determine that a pulse rate of the sensed pulse rate signal, within a predetermined time period, is erroneous. The processor circuitry is configured to process the erroneous pulse rate to create a modified sensed pulse rate signal. The processor circuitry is configured to determine an HRV value based on the modified pulse rate signal over the predetermined time period and output information indicative of the determined HRV value.

Abstract: An example device for determining one or more transient decelerations includes a memory configured to store a sensed pulse rate signal indicative of one or more sensed pulse rates and processing circuitry. The processing circuitry is configured to determine that an amplitude threshold is crossed by a sensed pulse rate signal indicative of one or more sensed pulse rates. The processing circuitry also is configured to, from a time the amplitude threshold is crossed, determine that a pulse rate returns to within a range of a baseline pulse rate within a number of samples or a time period. The processing circuitry is also configured to, based on the pulse rate returning to within the range of the baseline pulse rate, from the time the amplitude threshold is crossed, within the number of samples or the time period, determine a transient deceleration.

Abstract: System and methods for video-based neonatal patient monitoring are described. Methods for neonatal patient monitoring can generally include use of a non-contact detector, such as a depth-sensing camera, to obtain data pertaining to the neonate that can then be used to calculate or otherwise determine a neonate patient breathing parameter, such as respiratory volume. The method further includes monitoring the breathing parameter to identify the occurrence of a neonate breathing event, such as apnea, and initiating a neonate stimulation event when a breathing event is identified. The stimulation event can include, e.g., vibration, auditory signals, visual signals, and/or other types of tactile signals. The systems and methods can use additional monitoring apparatus to monitor additional neonate health parameters, and incorporate this additional information into the decision of when a stimulation event should be initiated.

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: Methods and systems for non-contact monitoring of a patient to determine a respiratory parameter such as respiration rate. The systems and methods receive a depth signal from the patient to determine patient movement indicative of respiration. The methods include analyzing multiple regions in a region of interest (ROI) to determine whether or not respiration is occurring in the analyzed region, and preparing a mask with the regions determined to have respiration. The mask is used to determine the respiratory parameter of the patient in the masked ROI.

Abstract: Implementations described herein disclose a method of determining how well predictions of an impending hypoxia are reported in real-time, so that a user has higher confidence in the reported predictions.

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: 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: Systems and methods for aiding a clinician in the proper positioning, placing or otherwise locating of a non-contact detector component of a non-contact patient monitoring system are described. The systems and methods may employ a targeting aid superimposed on a display screen component of the non-contact patient monitoring system, the targeting aid being designed to assist the clinician in properly locating the non-contact detector for proper and accurate functioning of the non-contact patient monitoring system. The systems and methods described herein may also employ a bendable mounting arm to which the non-contact detector is attached such that the non-contact detector can be easily moved into the proper location when used in conjunction with the targeting aid superimposed on the display.

Abstract: Implementations described herein disclose an artificial intelligence (AI) based method for generating an oxygen saturation level output signal using the trained neural network. In one implementation, the method includes receiving a photoplethysmographic (PPG) signal, the PPG signal including a red PPG signal and an infrared PPG signal, generating an input feature matrix by performing time-frequency transform of the PPG signal, training a neural network using the input feature matrix and an oxygen saturation level input signal, and generating an oxygen saturation level output signal using the trained neural network.

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: A monitor configured to monitor autoregulation includes a memory encoding one or more processor-executable routines and a processor configured to access and execute the one or more routines encoded by the memory. When executed, the routines cause the processor to receive one or more physiological signals from a patient, determine a measure indicative of an autoregulation status of the patient based on the one or more physiological signals, generate an autoregulation alarm indicative of an impaired autoregulation status when the measure exceeds a predetermined threshold for more than a predetermined period of time.

Abstract: In some examples, a system includes an oxygen saturation sensing device configured to sense an oxygen saturation level of a patient and processing circuitry. The processing circuitry may be configured to receive a signal indicative of the oxygen saturation level of the patient, determine that the signal indicates the oxygen saturation level is at or below a desaturation threshold, and in response to determining the oxygen saturation level of the patient is at or below the desaturation threshold, 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: 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: 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: 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: The present technology relates to patient monitoring systems and methods using plural PPG sensors in contact with a patient at different locations, wherein a comparison of the PPG data is performed to calculate a differential pulse transit time (DPTT) between the first and second locations, followed by a determination of continuous non-invasive blood pressure (CNIBP) using the PPG data from the plural and the calculated DPTT.

Abstract: In some examples, a device includes processing circuitry configured to receive first and second signals indicative of first and second physiological parameters and determine a trendline function based on values of first and second physiological parameters. The processing circuitry is further configured to determine transformed values of the first physiological parameter based on the trendline function. The processing circuitry is configured to determine correlation coefficient values for the transformed values of the first physiological parameter and the values of the second physiological parameter. The processing circuitry is further configured to determine a limit of autoregulation of the patient based on the correlation coefficient values. The processing circuitry is configured to determine an autoregulation status based on the estimate of the limit of autoregulation and output, for display, an indication of the autoregulation status.

Abstract: A monitor configured to monitor autoregulation includes a memory encoding one or more processor-executable routines and a processor configured to access and execute the one or more routines encoded by the memory. When executed, the routines cause the processor to receive one or more physiological signals from a patient, determine a measure indicative of an autoregulation status of the patient based on the one or more physiological signals, generate an autoregulation alarm indicative of an impaired autoregulation status when the measure exceeds a predetermined threshold for more than a predetermined period of time.