Method of estimating the actual ECG of a patient during CPR

• US 20040210172A1
• Filed: 05/12/2004
• Published: 10/21/2004
• Est. Priority Date: 10/25/2002
• Status: Active Grant

• Current Assignee: Zoll Circulation Incorporated (Asahi Kasei Corporation)
• Original Assignee: Revivant Corporation (Asahi Kasei Corporation)
• Inventors: Berger, Ronald D., Halperin, Henry R., Sherman, Darren R., Palazzolo, James Adam
• Granted Patent: US 7,476,206 B2
• Time in Patent Office: Days
• US Class Current: 601/41
• CPC Class Codes: A61B 5/053 Measuring electrical impedance or conductance of a portion of the body
• CPC Class Codes: A61B 5/0535 Impedance plethysmography for measuring blood flow A61B5/0295
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/339 Displays specially adapted therefor
• CPC Class Codes: A61B 5/721 using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
• CPC Class Codes: A61B 5/7242 using integration
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61H 2201/5007 computer controlled
• CPC Class Codes: A61H 2201/501 connected to external computer devices or networks
• CPC Class Codes: A61H 2201/5012 using the internet
• CPC Class Codes: A61H 2201/5043 Displays
• CPC Class Codes: A61H 2201/5048 Audio interfaces, e.g. voice or music controlled
• CPC Class Codes: A61H 2201/5058 Sensors or detectors
• CPC Class Codes: A61H 2201/5084 Acceleration sensors
• CPC Class Codes: A61H 2201/5097 wireless
• CPC Class Codes: A61H 2230/04 Heartbeat characteristics, e.g. E.G.C., blood pressure modulation
• CPC Class Codes: A61H 2230/08 Other bio-electrical signals
• CPC Class Codes: A61H 2230/40 Respiratory characteristics
• CPC Class Codes: A61H 31/005 with feedback for the user
• CPC Class Codes: A61H 31/006 Power driven
• CPC Class Codes: A61H 31/007 : Manual driven
• CPC Class Codes: A61H 31/008 : Supine patient supports or bases, e.g. improving air-way access to the lungs
• CPC Class Codes: A61M 16/00 : Devices for influencing the respiratory system of patients by gas treatment, e.g. mouth-to-mouth respiration; Tracheal tubes stimulating the respiratory movement by mechanical, pneumatic or electrical means, iron lungs combined with gas breathing means A61H31/00
• CPC Class Codes: A61M 16/0078 : Breathing bags
• CPC Class Codes: A61N 1/39044 : in combination with cardiopulmonary resuscitation [CPR] therapy
• CPC Class Codes: G06F 2218/04 : Denoising
• CPC Class Codes: Y10S 128/901 : Suppression of noise in electric signal
• CPC Class Codes: Y10S 601/08 : Artificial respiration with computer control
• CPC Class Codes: Y10S 601/09 : including biological sensors
• CPC Class Codes: Y10S 601/10 : Artificial respiration combined with non-artificial respiration therapy

Abstract

A method of processing a raw acceleration signal, measured by an accelerometer-based compression monitor, to produce an accurate and precise estimated actual depth of chest compressions. The raw acceleration signal is filtered during integration and then a moving average of past starting points estimates the actual current starting point. An estimated actual peak of the compression is then determined in a similar fashion. The estimated actual starting point is subtracted from the estimated actual peak to calculate the estimated actual depth of chest compressions. In addition, one or more reference sensors (such as an ECG noise sensor) may be used to help establish the starting points of compressions. The reference sensors may be used, either alone or in combination with other signal processing techniques, to enhance the accuracy and precision of the estimated actual depth of compressions.

Claims

1. A method of estimating an actual ECG signal of a patient while performing chest compressions with an automatic chest compressions device, wherein the method comprises the steps of:

providing an ECG sensor capable of measuring an ECG signal of the patient, said ECG sensor producing a measured ECG signal having an actual component and a noise component;

providing an automatic chest compression device disposed to provide chest compressions to the patient, said chest compression device having a load sensor capable of determining the presence of a chest compression when the load sensed by the load sensor exceeds a predetermined value, said load sensor producing a compression signal corresponding the presence a chest compression;

performing compressions;

providing the measured ECG signal to a system identifier;

providing the compression signal to the system identifier;

estimating the noise component of the measured ECG signal with the system identifier by processing the measured ECG signal and the compression signal;

providing the measured ECG signal and the estimated noise component of the measured ECG signal to a means for combining signals;

calculating the estimated actual ECG with the means for combining signals by combining the measured ECG signal and the noise component of the measured ECG signal.

2. The method of claim 1 wherein the step of providing an automatic chest compression device having a load sensor comprises providing an automatic chest compression device having a load sensor that is disposed beneath the patient during compressions.

3. The method of claim 1 wherein the system identifier comprises a moving average filter.

4. The method of claim 1 wherein the system identifier comprises an autoregressive moving average filter.

5. The method of claim 1 wherein the system identifier comprises an autoregressive moving average with truncated derivative filter.

6. The method of claim 1 wherein the system identifier comprises a Kalman filter.

7. The method of claim 1 wherein the system identifier comprises a recursive least squares filter

8. The method of claim 1 wherein the system identifier comprises a recursive instrumental variable filter.

9. The method of claim 1 wherein the system identifier comprises a recursive prediction error filter.

10. The method of claim 1 wherein the system identifier comprises a recursive pseudolinear regression filter.

11. The method of claim 1 wherein the system identifier comprises a recursive Kalman filter for time-varying systems filter.

12. The method of claim 1 wherein the system identifier comprises a recursive Kalman filter with parametric variation filter.

13. A method of estimating an actual ECG signal of a patient while performing chest compressions with an automatic chest compressions device, wherein the method comprises the steps of:

providing an ECG sensor capable of measuring an ECG signal of the patient, said ECG sensor producing a measured ECG signal having an actual component and a noise component;

providing an automatic chest compression device disposed to provide chest compressions to the patient, said chest compression device having an encoder capable of determining the presence of a chest compression, said encoder producing a compression signal corresponding the

presence a chest compression;

performing compressions;

providing the measured ECG signal to a system identifier;

providing the compression signal to the system identifier;

estimating the noise component of the measured ECG signal with the system identifier by processing the measured ECG signal and the compression signal;

providing the measured ECG signal and the estimated noise component of the measured ECG signal to a means for combining signals;

calculating the estimated actual ECG with the means for combining signals by combining the measured ECG signal and the noise component of the measured ECG signal.

14. The method of claim 13 wherein the step of providing an automatic chest compression device having an encoder comprises providing an automatic chest compression device having an optical encoder.

15. The method of claim 13 wherein the step of providing an automatic chest compression device having an encoder comprises providing an automatic chest compression device having a rotary encoder.

16. The method of claim 13 wherein the system identifier comprises a moving average filter.

17. The method of claim 13 wherein the system identifier comprises an autoregressive moving average filter.

18. The method of claim 13 wherein the system identifier comprises an autoregressive moving average with truncated derivative filter.

19. The method of claim 13 wherein the system identifier comprises a Kalman filter.

20. The method of claim 13 wherein the system identifier comprises a recursive least squares filter

21. The method of claim 13 wherein the system identifier comprises a recursive instrumental variable filter.

22. The method of claim 13 wherein the system identifier comprises a recursive prediction error filter.

23. The method of claim 13 wherein the system identifier comprises a recursive pseudolinear regression filter.

24. The method of claim 13 wherein the system identifier comprises a recursive Kalman filter for time-varying systems filter.

25. The method of claim 13 wherein the system identifier comprises a recursive Kalman filter with parametric variation filter.

26. A method of estimating an actual ECG signal of a patient while performing chest compressions with an automatic chest compressions device, wherein the method comprises the steps of:

providing an ECG sensor capable of measuring an ECG signal of the patient, said ECG sensor producing a measured ECG signal having an actual component and a noise component;

providing an automatic chest compression device disposed to provide chest compressions to the patient, said chest compression device having an accelerometer capable of determining the presence of a chest compression, said accelerometer producing a compression signal corresponding the presence a chest compression;

performing compressions;

providing the measured ECG signal to a system identifier;

providing the compression signal to the system identifier;

estimating the noise component of the measured ECG signal with the system identifier by processing the measured ECG signal and the compression signal;

providing the measured ECG signal and the estimated noise component of the measured ECG signal to a means for combining signals;

calculating the estimated actual ECG with the means for combining signals by combining the measured ECG signal and the noise component of the measured ECG signal.

27. The method of claim 26 wherein the system identifier comprises a moving average filter.

28. The method of claim 26 wherein the system identifier comprises an autoregressive moving average filter.

29. The method of claim 26 wherein the system identifier comprises an autoregressive moving average with truncated derivative filter.

30. The method of claim 26 wherein the system identifier comprises a Kalman filter.

31. The method of claim 26 wherein the system identifier comprises a recursive least squares filter

32. The method of claim 26 wherein the system identifier comprises a recursive instrumental variable filter.

33. The method of claim 26 wherein the system identifier comprises a recursive prediction error filter.

34. The method of claim 26 wherein the system identifier comprises a recursive pseudolinear regression filter.

35. The method of claim 26 wherein the system identifier comprises a recursive Kalman filter for time-varying systems filter.

36. The method of claim 26 wherein the system identifier comprises a recursive Kalman filter with parametric variation filter.