Abstract: Methods and systems are provided for using time-frequency warping to analyze a physiological signal. One embodiment includes applying a warping operator to the physiological signal based on the energy density of the signal. The warped physiological signal may be analyzed to determine whether non-physiological signal components are present. Further, the same warping operator may be applied to signal quality indicators, and the warped physiological signal may be analyzed based on the warped signal quality indicators. Non-physiological signal components, or types of non-physiological noise sources, may be identified based on a comparison of the physiological signal with the signal quality indicators. Non-physiological signal components may also be identified based on a neural network of known noise functions. In some embodiments, the non-physiological signal components may be removed to increase accuracy in estimating physiological parameters.

Abstract: Methods and systems are provided for using time-frequency warping to analyze a physiological signal. One embodiment includes applying a warping operator to the physiological signal based on the energy density of the signal. The warped physiological signal may be analyzed to determine whether non-physiological signal components are present. Further, the same warping operator may be applied to signal quality indicators, and the warped physiological signal may be analyzed based on the warped signal quality indicators. Non-physiological signal components, or types of non-physiological noise sources, may be identified based on a comparison of the physiological signal with the signal quality indicators. Non-physiological signal components may also be identified based on a neural network of known noise functions. In some embodiments, the non-physiological signal components may be removed to increase accuracy in estimating physiological parameters.