Method, system, and apparatus for cardiovascular signal analysis, modeling, and monitoring
Disclosed embodiments of the invention include a method, system, and apparatus to monitor cardiovascular signals such as arterial blood pressure (ABP), pulse oximetry (POX), and intracranial pressure (ICP). The system can be used to calculate and monitor useful clinical information such as heart rate, respiratory rate, pulse pressure variation (PPV), harmonic phases, pulse morphology, and for artifact removal. The method uses a statistical state-space model of cardiovascular signals and a generalized Kalman filter (EKF) to simultaneously estimate and track the cardiovascular parameters of interest such as the cardiac fundamental frequency and higher harmonics, respiratory fundamental frequency and higher harmonics, cardiac component harmonic amplitudes and phases, respiratory component harmonic amplitudes and phases, and PPV.
1. A computer implemented method for monitoring and analysis of cardiovascular signals based on state-space estimation methods, comprising:
(a) implementing a statistical state-space model for cardiovascular signals, said statistical model includes a method for limiting said cardiovascular signals' instantaneous frequency to physiologic limits based on enforcing limits on one or more estimated state variables, and said state-space method makes use of an instantaneous phase update clipping function based on innovations e(n) to improve stability and robustness, wherein said innovations e(n)=y(n)−h[{circumflex over (χ)}(n|n−1)] are obtained by subtracting the predicted measurement h[{circumflex over (χ)}(n|n−1)] based on the predicted state {circumflex over (χ)}(n|n−1)from the observations y(n);
(b) initializing said statistical state-space model; and
(c) processing said cardiovascular signals using a device with a processor to estimate a set of clinically relevant cardiovascular parameters based on said statistical state-space model using a state-space estimation method.
2. A method as recited in claim 1 , wherein said state-space method is implemented using an Extended Kalman Filter (EKF).
3. A method as recited in claim 1 , wherein said state-space method is implemented using a particle filter.
4. A method as recited in claim 1 , wherein said state-space method is implemented using a sigma-point filter.
5. A method as recited in claim 1 , wherein said set of clinically relevant cardiovascular parameters include heart rate, respiratory rate, pulse pressure variation, amplitude and phases of higher cardiac harmonics, amplitude and phases of higher respiratory harmonics, pulse morphology, amplitude modulation index due to respiration, and frequency modulation index due to respiration.
6. A method as recited in claim 1 , wherein said statistical state-space model and said state-space estimation method is used to remove artifacts and noise from said cardiovascular signals.
7. A method as recited in claim 1 , wherein said method is implemented in firmware or software as means for determining cardiovascular parameters in a cardiovascular apparatus, medical device, or cardiovascular analysis system which includes integrated means for (1) estimating and tracking heart rate from cardiovascular signals, (2) estimating and tracking respiratory rate from cardiovascular signals, (3) model-based filtering, artifact removal, and interpolation, (4) cardiovascular signal decomposition, characterization, and tracking of pulse morphology, and (5) pulse pressure variation estimation.
8. A method as recited in claim 7 , wherein said method includes means for estimating and tracking pulse pressure variation in mechanically ventilated patients.
9. A method as recited in claim 1 , wherein said method includes means for estimating and tracking pulse pressure variation from arterial blood pressure or plethysmography signals.
10. A method as recited in claim 9 , wherein said method includes means for estimating and tracking pulse pressure variation in mechanically ventilated patients during regions of abrupt hemodynamic changes from arterial blood pressure or plethysmography signals.
11. A computer implemented method for predicting fluid responsiveness and guiding fluid therapy from cardiovascular signals, comprising:
(a) implementing a statistical state-space model for cardiovascular signals, said statistical model includes a method for limiting said cardiovascular signals' instantaneous frequency to physiologic limits based on enforcing limits on one or more estimated state variables, and said state-space method makes use of an instantaneous phase update clipping function based on innovations e(n) to improve stability and robustness, wherein said innovations e(n)=y(n)−h[{circumflex over (χ)}(n|n−1)] are obtained by subtracting the predicted measurement h[{circumflex over (χ)}(n|n−1)] based on the predicted state {circumflex over (χ)}(n|n−1) from the observations y(n);
(b) initializing said statistical state-space model;
(b) initializing a generalized Kalman filter with initial values for said state-space model parameters;
(c) estimating pulse pressure variation parameter using said generalized Kalman filter and said statistical state-space model implemented in a device with a processor from input arterial blood pressure or plethysmography signals; and
(d) predicting fluid status and guiding fluid therapy based on said pulse pressure variation parameter.
12. A medical apparatus for cardiovascular signal monitoring and analysis, comprising:
(a) a processor configured for (1) implementing a statistical state-space model for cardiovascular signals, said statistical model includes a method for limiting said cardiovascular signals' instantaneous frequency to physiologic limits based on enforcing limits on one or more estimated state variables, and said state-space method makes use of an instantaneous phase update constrain function based on innovations e(n) to improve stability and robustness, wherein said innovations e(n)=y(n)−h[{circumflex over (χ)}(n|n−1)] are obtained by subtracting the predicted measurement h[{circumflex over (χ)}(n|n−1)] based on the predicted state {circumflex over (χ)}(n|n−1) from the observations y(n); (2) initializing said statistical state-space model; and (3) processing said cardiovascular signals using a device with a processor to estimate a set of clinically relevant cardiovascular parameters based on said statistical state-space model using a state-space estimation method.
13. The apparatus of claim 12 , wherein said state-space method is implemented using an Extended Kalman Filter (EKF).
14. The apparatus of claim 12 , wherein said state-space method is implemented using a particle filter.
15. The apparatus of claim 12 , wherein said state-space method is implemented using a sigma-point filter.
16. The apparatus of claim 12 , wherein said set of clinically relevant cardiovascular parameters include heart rate, respiratory rate, pulse pressure variation, amplitude and phases of higher cardiac harmonics, amplitude and phases of higher respiratory harmonics, pulse morphology, amplitude modulation index due to respiration, and frequency modulation index due to respiration.
17. The apparatus of claim 12 , wherein said statistical state-space model and said state-space estimation method is used to remove artifacts and noise from said cardiovascular signals.
18. The apparatus of claim 12 , wherein said method is implemented as means for (1) estimating and tracking heart rate from cardiovascular signals, (2) estimating and tracking respiratory rate from cardiovascular signals, (3) model-based filtering, artifact removal, and interpolation, (4) cardiovascular signal decomposition, characterization, and tracking of pulse morphology, and (5) pulse pressure variation estimation.
19. The apparatus of claim 12 , wherein said method includes means for estimating and tracking pulse pressure variation from arterial blood pressure or plethysmography signals.
20. The apparatus of claim 19 , wherein said method includes means for estimating and tracking pulse pressure variation in mechanically ventilated patients.