METHOD, SYSTEM, AND APPARATUS FOR CARDIOVASCULAR SIGNAL ANALYSIS, MODELING, AND MONITORING

• US 20090069647A1
• Filed: 08/29/2008
• Published: 03/12/2009
• Est. Priority Date: 08/31/2007
• Status: Active Grant

• Current Assignee: APDM, Inc., Oregon State University (Oregon University System), Wearables IP Holdings LLC
• Original Assignee: APDM, Inc., Oregon State University (Oregon University System)
• Inventors: Riobo Aboy, Pedro Mateo, McNames, James
• Granted Patent: US 8,057,398 B2
• Time in Patent Office: Days
• US Class Current: 600/301
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/031 Intracranial pressure
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/412 Detecting or monitoring sepsis
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence

Abstract

The present invention provides 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.

Claims

1. A method of monitoring and analysis of cardiovascular signals based on statistical models and generalized Kalman filters, comprising:

(a) implementing a statistical state-space model for cardiovascular signals;

(b) initializing a generalized Kalman filter with initial values based on domain knowledge for said state-space model parameters; and

(c) processing collected cardiovascular signals to estimate clinically relevant cardiovascular parameters using said generalized Kalman filter based on said statistical state-space model and said initial values for said state-space model parameters.

2. A method as recited in claim 1, wherein said generalized Kalman filter is implemented using an Extended Kalman Filter (EKF).

3. A method as recited in claim 1, wherein said generalized Kalman filter is implemented using a particle filter.

4. A method as recited in claim 1, wherein said generalized Kalman filter is implemented using a sigma-point filter.

5. A method as recited in claim 1, wherein said clinically relevant cardiovascular parameters automatically estimated by said Kalman filter include heart rate, respiratory rate, pulse pressure variation, amplitude and phases of higher cardiac harmonics, amplitude and phases of higher respiratory harmonics, amplitude modulation index due to respiration, and frequency modulation index due to respiration.

6. 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.

7. 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.

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 8, 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.

10. A method for predicting fluid responsiveness and guiding fluid therapy, comprising:

(a) implementing a statistical state-space model for cardiovascular signals;

(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 from input arterial blood pressure or plethysmography signals; and

(d) predicting fluid status and guiding fluid therapy based on said pulse pressure variation parameter.