Covidien LP

Addison, Paul

Montgomery, Dean

Watson, James

Dean Montgomery

Paul Stanley Addison

Andre Antunes

James Nicholas Watson

Philip C Smit

André Antunes

Peter M Galen

Dominique Jacquel

Michael J Addison

Methods and systems for monitoring a patient's position using non-contact patient monitoring systems are described. In some embodiments, depth sensing cameras are used to obtain depth images of a patient, wherein data can be extracted from the depth images and processed in various ways to make a determination as to the patient's position, including monitoring when and how often a patient changes position. Various alarms systems can also be integrated into the systems and methods described herein in order to alert clinicians regarding, for example, patients moving too infrequently, patients moving too frequently, or patients moving into undesirable positions in view of the condition being treated. AI-based patient monitoring systems used for determining patient position are also described.

Patient position monitoring methods and systems

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.

Enhanced image for non-contact monitoring

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.

System and method for identifying blood pressure zones during autoregulation monitoring

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.

Non-contact depth sensing monitoring in vehicles

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

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).

Desaturation severity prediction and alarm management

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 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.

System and method for providing blood pressure safe zone indication during autoregulation monitoring

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.

Vision-based patient stimulus monitoring and response system and method utilizing visual images

A patient monitoring system may include processing circuitry configured to receive an indication of a surgical event, obtain a nociception parameter of a patient, compare the nociception parameter of the patient to a nociception threshold to detect a nociception event, determine whether the surgical event corresponds to the nociception event, and in response to determinizing whether the surgical event corresponds to the nociception event, provide an indication to adjust an amount of analgesic administered to the patient.

Nociception stimulus feedback control for drug titration during surgery

A system for monitoring autoregulation includes an oxygen saturation sensor configured to obtain an oxygen saturation signal indicative of an oxygen saturation of a patient. The system also includes a controller having a processor configured to receive a blood pressure signal indicative of a blood pressure of the patient and the oxygen saturation signal, determine a change in the oxygen saturation signal and a change in the blood pressure signal over a period of time, and provide an indication that the patient's autoregulation is intact if the oxygen saturation changes by more than an oxygen saturation threshold and if the blood pressure changes by less than a blood pressure threshold during the period of time.

Systems and methods of monitoring autoregulation

Implementations described herein disclose a method of AI-based video tagging for alarm management including receiving, using a processor, a video stream, the video stream comprising a sequence of images for at least a portion of a patient; determining, using the processor, a physiological parameter for the patient based on the sequence of images, detecting, using machine learning, the presence of a noise object and setting an interaction-flag to a positive value in response to detecting the noise object; comparing a quality level of the sequence of images with a threshold quality level; and modifying an alarm level based on the value of the interaction-flag and comparison of the quality level of the sequence of depth images with the threshold quality level.

AI-based video tagging for alarm management

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.

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Edinburgh, United Kingdom

Dean Montgomery