Abstract:
Implementations described herein disclose a method of predicting oxygen level desaturation including generating an input sequence of oxygen levels based on an input signal sequence, the input signals indicative of a physiological condition of a patient, generating an input feature sequence based on at least one of the input signal sequence and the input sequence of oxygen levels, generating, using a neural network, a predicted value sequence of the oxygen levels based on the input feature sequence, comparing the predicted value sequence of the oxygen levels with the input sequence of oxygen levels for a predetermined temporal window to generate a predicted sequence confidence value, and generating, in response to determining that the predicted sequence confidence value is above a threshold confidence value, an oxygen level desaturation prediction based on the predicted value sequence.
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 state of the patient based on the one or more physiological signals, calculate an autoregulation state value based on the measure and assign the autoregulation state value to corresponding blood pressures, generate an autoregulation profile of the patient, wherein the autoregulation profile comprises autoregulation state values across blood pressures; and identify a blood pressure safe zone of the autoregulation profile.
Abstract:
In some examples, a system (100) includes an oxygen saturation sensing device (150) configured to sense an oxygen saturation level of a patient (101) and processing circuitry (110). The processing circuitry (110) 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 (124), 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. This may help prevent outputting an indication in cases which are not medically meaningful (false alarms) and in which the oxygen saturation level of the patient will only briefly dip below the desaturation threshold before increasing back above the desaturation threshold.
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 photopl ethy smographi c (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:
A system for continuous non-invasive blood pressure monitoring may include processing circuitry configured to determine calibration data for a continuous non-invasive blood pressure model at a calibration point, receive, from an oxygen saturation sensing device, a PPG signal at a particular time subsequent to the calibration point, derive values of the set of metrics for the patient from the PPG signal, and determine, using the continuous non-invasive blood pressure model and based at least in part on inputting the calibration data determined at the calibration point, the values of the set of metrics, and an elapsed time at the particular time since the calibration point into the continuous non-invasive blood pressure model, a blood pressure of the patient at the particular time.
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:
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, 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:
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 (36) 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 (40) 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:
A method (170) for monitoring autoregulation includes using a processor (24) for receiving one or more physiological signals (172, 174), determining a correlation-based measure indicative of the patient's autoregulation based on the one or more physiological signals (175), calculating a data clustering metric indicative of a distribution of the correlation-based measure within a window of blood pressures (178), and determining whether the window of blood pressures is within an intact autoregulation zone or an impaired autoregulation zone based at least in part on the data clustering metric (182).