Covidien LP

Andre Antunes

Paul Stanley Addison

Dean Montgomery

Philip C Smit

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.

Blood pressure and autoregulation monitoring

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.

Non-cerebral organ autoregulation status determination

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 an altered blood pressure value indicative of a reference blood pressure of the patient at a reference site based on the blood pressure signal. The altered blood pressure value may enable a more accurate subsequent determination of a value indicative of the autoregulation status of the patient compared to an unaltered blood pressure value. In some examples, the processing circuitry may provide a signal indicative of the autoregulation status of the patient to an output device to enable a clinician to monitor the autoregulation status of the patient.

Mitigating input blood pressure variability in autoregulation monitoring

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.

Determining changes to autoregulation

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.

Autoregulation monitoring using deep learning

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. Specifically, the method of predicting oxygen level desaturation disclosed herein includes 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.

System and method of predicting oxygen level desaturation

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.

Oxygen saturation monitoring using artificial intelligence

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.

Calibration for continuous non-invasive blood pressure monitoring using artificial intelligence

In some examples, a device includes sensing circuitry configured to receive one or more signals from a patient and processing circuitry configured to determine a plurality of autoregulation state values associated with a plurality of blood pressure values based on the one or more signals. The processing circuitry is further configured to determine that a first autoregulation state value of the plurality of autoregulation state values is anomalous based on other autoregulation state values of the plurality of autoregulation state values. The processing circuitry is also configured to modify the first autoregulation state value in response to determining that the first autoregulation state value is anomalous and determine an autoregulation status of the patient based on the plurality of autoregulation state values including the modified first autoregulation state value.

Identifying anomalous autoregulation state values

In some examples, devices, systems, and techniques are configured to compensate for changes in sensed blood pressure due to issues such as blood pressure sensor movement. For example, processing circuitry of a device may determine a difference value between a first blood pressure value representative of a blood pressure at a first time and a second blood pressure value representative of the blood pressure at a second time. Responsive to determining that the difference value is greater than or equal to a threshold value, the processing circuitry may generate updated blood pressure values by applying an offset value to the second blood pressure value and subsequently received blood pressure values that compensates for the difference value between the first blood pressure value and the second blood pressure value.

Growing community of inventors

Edinburgh, United Kingdom

Andre Antunes