Abstract:
A medical sensor includes a first set of optical components configured to obtain a first set of signals for determining a first regional oxygen saturation measurement. The first set of optical components includes a first emitter, a first detector separated from the first emitter by a first distance along a first axis, and a second detector separated from the first emitter by a second distance along the first axis, wherein the second distance is greater than the first distance. The sensor also includes a second set of optical components configured to obtain a second set of signals for determining a second regional oxygen saturation measurement. The second set of optical components includes a second emitter and a third detector separated from the second emitter by a third distance along a second axis, different from the first axis.
Abstract:
A physiological monitoring system may receive a sensor signal from a physiological sensor. The system may determine a first and second change metric based on the sensor signal, and may determine a venous signal based on the change metrics. In some embodiments, the sensor signal may be a photoplethysmograph signal that includes both arterial and venous information. By subtracting a second change metric from a first change metric, arterial contributions may be substantially removed, resulting in a signal primarily comprising venous information. The venous signal may be indicative of changes in the venous blood, and may be used to determine a physiological parameter, for example, blood pressure. The venous signal may also be used to trigger an event, for example, calibration of a blood pressure measurement.
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 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.
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 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.
Abstract:
A method for monitoring autoregulation includes, using a processor, receiving a blood pressure signal, a regional oxygen saturation signal, and a blood volume signal from a patient. The method also includes determining a first linear correlation between the blood pressure signal and the regional oxygen saturation signal and determining a second linear correlation between the blood pressure signal and the blood volume signal. The method also includes determining a confidence level associated with the first linear correlation based at least in part on the second linear correlation and providing a signal indicative of the patient's autoregulation status to an output device based on the linear correlation and the confidence level.
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:
Methods and systems are presented for determining physiological information in a physiological monitor. A physiological signal (e.g., an EEG signal) received from a subject is wavelet transformed and first and second related features that vary in scale over time are identified in the transformed signal. First and second coupled ridges of the respective first and second related features may also be identified in the transformed signal. A non-stationary relationship parameter is determined and is indicative of the relationship between the first and second features and/or between the first and second ridges. Physiological information, which may be indicative of a level of awareness of a subject, is determined based on the non-stationary relationship parameter. This physiological information may be used, for example, in an operating room to monitor/regulate the subject's anesthetic state while under general anesthesia or in an intensive care unit to monitor the subject's sedateness and administer medication accordingly.
Abstract:
Systems, methods, sensors, and software for providing enhanced measurement and detection of patient pain response are provided herein. In a first example, a measurement system is provided that includes a capacitive system configured to measure a capacitance signal of tissue of the patient using a capacitive sensor applied to the tissue of the patient. The measurement system also includes a patient monitor configured to measure an electrical signal representing brain activity of the patient using a brain activity sensor applied to the tissue of the patient. The measurement system also includes a processing system configured to determine pain metrics based at least on the capacitance signal and the electrical signal, and determine a pain response of the patient based at least on the pain metrics and pain calibration information for the patient.
Abstract:
In some examples, a device includes processing circuitry configured to receive first and second signals indicative of first and second physiological parameters, respectively, of a patient. The processing circuitry is also configured to determine a first estimate of a limit of autoregulation of the patient based on the first and second signals. The processing circuitry is further configured to determine a difference between the first estimate of the limit of autoregulation and one or more other estimates of the limit of autoregulation. The processing circuitry is configured to determine a weighted average of the first estimate and a previous value of the limit of autoregulation based on the difference between the first estimate and the one or more other estimates. The processing circuitry is configured to determine an autoregulation status based on the weighted average and output, for display via the display, an indication of the autoregulation status.
Abstract:
Methods and systems are presented for determining physiological information in a physiological monitor. A physiological signal (e.g., an EEG signal) received from a subject is wavelet transformed and first and second related features that vary in scale over time are identified in the transformed signal. First and second coupled ridges of the respective first and second related features may also be identified in the transformed signal. A non-stationary relationship parameter is determined and is indicative of the relationship between the first and second features and/or between the first and second ridges. Physiological information, which may be indicative of a level of awareness of a subject, is determined based on the non-stationary relationship parameter. This physiological information may be used, for example, in an operating room to monitor/regulate the subject's anesthetic state while under general anesthesia or in an intensive care unit to monitor the subject's sedateness and administer medication accordingly.