Abstract:
Methods and systems are provided for deriving and analyzing shape metrics, including skewness metrics, from physiological signals and their derivatives to determine measurement quality, patient status and operating conditions of a physiological measurement device. Such determinations may be used for any number of functions, including indicating to a patient or care provider that the measurement quality is low or unacceptable, alerting a patient or care provider to a change in patient status, triggering or delaying a recalibration of a monitoring device, and adjusting the operating parameters of a monitoring system.
Abstract:
Systems and methods for detecting and monitoring arrhythmias from a signal are provided. A signal processing system may transform a signal using a wavelet transformation and analyze changes in features of the transformed signal to detect pulse rhythm abnormalities. For example, the system may detect pulse rhythm abnormalities by analyzing energy parameters, morphology changes, and pattern changes in the scalogram of a PPG signal. Further, the system may detect pulse rhythm abnormalities by analyzing both the PPG signal and its corresponding scalogram. Physiological information, such as cardiac arrhythmia, may be derived based on the detected pulse rhythm abnormality.
Abstract:
A patient monitoring system may receive a photoplethysmograph (PPG) signal including samples of a pulse waveform. The PPG signal may demonstrate morphology changes based on respiration. The system may calculate morphology metrics from the PPG signal, the first derivative of the PPG signal, the second derivative of the PPG signal, or any combination thereof. The morphology metrics may demonstrate amplitude modulation, baseline modulation, and frequency modulation of the PPG signal that is related to respiration. Morphology metric signals generated from the morphology metrics may be used to determine respiration information such as respiration rate.
Abstract:
A patient monitoring system may be configured to use template matching in determining physiological parameters. A physiological signal may be monitored, and a wavelet transform may be performed. The wavelet transform, or parameters derived thereof such as energy distribution or relative phase difference, may be compared with one or more templates using template matching. Templates may be based on, for example, physiological data, mathematical models, or look-up tables, and may be pre-computed and stored. Physiological parameters may be determined based on the template matching results. Scale variability, confidence metrics, or both, may be used to aid in determining the physiological parameter.
Abstract:
Methods and systems are disclosed for tuning first and second wavelet functions to resolve at least one component of a signal. A first characteristic frequency corresponding to a first scale band of interest is determined, and a first wavelet function is tuned to the first characteristic frequency in at least a region of a first scale band of interest. A second characteristic frequency corresponding to a second scale band of interest is determined, and a second wavelet function is tuned to the second characteristic frequency in at least a region of the second scale band of interest. A signal is transformed for the first and second wavelet functions using a continuous wavelet transform to create a transform signal, and a scalogram is generated based at least in part on the transformed signal.