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:
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:
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:
Systems and methods provided relate to patient sensors and/or patient monitors that recognize and/or identify a patient with physiological signals obtained from the sensor. A scalogram may be produced by applying a wavelet transform for the physiological signals obtained from the sensor. The scalogram may be a three dimensional model (having time, scale, and magnitude) from which certain physiological information may be obtained. For example, unique human physiological characteristics, also known as biometrics, may be determined from the scalograms. More specifically, monitoring the changes in the morphology of the photoplethysmographic (PPG) waveform transforms (e.g., scalogram) may determine patient-specific information that may be used to recognize and/or identify the patient, and that may be used to determine a proper or improper association between the patient and the wireless sensor and/or patient monitor.
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:
Systems and methods provided relate to patient sensors and/or patient monitors that recognize and/or identify a patient with physiological signals obtained from the sensor. A scalogram may be produced by applying a wavelet transform for the physiological signals obtained from the sensor. The scalogram may be a three dimensional model (having time, scale, and magnitude) from which certain physiological information may be obtained. For example, unique human physiological characteristics, also known as biometrics, may be determined from the scalograms. More specifically, monitoring the changes in the morphology of the photoplethysmographic (PPG) waveform transforms (e.g., scalogram) may determine patient-specific information that may be used to recognize and/or identify the patient, and that may be used to determine a proper or improper association between the patient and the wireless sensor and/or patient monitor.
Abstract:
The present invention relates to physiological signal processing, and in particular to methods and systems for processing physiological signals to predict a fluid responsiveness of a patient. A medical monitor for monitoring a patient includes an input receiving a photoplethysmograph (PPG) signal representing light absorption by a patient's tissue. The monitor also includes a perfusion status indicator indicating a perfusion status of the PPG signal, and a fluid responsiveness predictor (FRP) calculator programmed to calculate an FRP value based on a respiratory variation of the PPG signal. The FRP calculator applies a correction factor based on the perfusion status indicator.
Abstract:
A method for reducing alarm fatigue, specifically, combining multiple physiological parameters, such as heart rate and respiratory rate, into one index number indicative of the patient's condition. The method includes detecting the severity of the patient's condition, generating, and displaying a scaled version of that index number relative to the severity of the patient's condition. The scaled index number is displayed on a patient monitor device. The scaled index number may have a size and color value.
Abstract:
Methods and systems are provided for determining fluid responsiveness in the presence of noise. The system may determine an instantaneous value indicative of fluid responsiveness. In some embodiments, the system may determine a difference between an instantaneous value indicative of fluid responsiveness and a previous value indicative of fluid responsiveness, and select an update characteristic based on whether the difference indicates that the fluid responsiveness is increasing or decreasing. In some embodiments, the system may determine a parameter indicative of fluid responsiveness based on the update characteristic and a previously reported value indicative of fluid responsiveness.
Abstract:
Systems and method for utilizing energy harvesting techniques to power a battery-less wireless medical sensor to perform intermittent operations are disclosed. The systems may include one or more sensing components configured to generate data related to one or more physiological parameters by performing intermittent measurements on a patient. The systems and method may include wireless communication circuitry configured to wirelessly transmit the data to a monitor. The monitor may be configured to operate with the battery-less wireless medical sensor or may download required operational algorithms if needed. The intermittent measurement and transmission may be asynchronously executed. The systems and method may include a processing device configured to determine when to perform the intermittent measurement and transmit data based at least upon a power source energy level, a rate at which to perform the intermittent measurement and transmit data, a prioritization, or a triggering event.