Adaptive epsilon-tube filter for blunt noise removal

• US 10,034,638 B2
• Filed: 02/22/2016
• Issued: 07/31/2018
• Est. Priority Date: 04/03/2015
• Status: Active Grant

• Current Assignee: Board of Regents of the University of Michigan (University of Michigan)
• Original Assignee: Board of Regents of the University of Michigan (University of Michigan)
• Inventors: Ansari, Sardar, Najarian, Kayvan, Ward, Kevin
• Primary Examiner(s): Manuel, George
• Application Number:
  US15/049,653
• Publication Number:
  US 20160287180A1
• Time in Patent Office: 890 Days
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/031 Intracranial pressure
• CPC Class Codes: A61B 5/0535 Impedance plethysmography for measuring blood flow A61B5/0295
• CPC Class Codes: A61B 5/0836 Measuring rate of CO2 production
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• 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 5/7214 using signal cancellation, e.g. based on input of two identical physiological sensors spaced apart, or based on two signals derived from the same sensor, for different optical wavelengths
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61B 5/7253 characterised by using transforms
• CPC Class Codes: A61B 8/488 involving Doppler signals
• CPC Class Codes: A61B 8/5223 for extracting a diagnostic or physiological parameter from medical diagnostic data for algorithms to analyse biomedical images G06T7/0012
• CPC Class Codes: A61B 8/5269 involving detection or reduction of artifacts for image enhancement algorithms G06T5/00
• CPC Class Codes: A61B 8/5276 due to motion
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

Techniques for motion artifact (MA) reduction in impedance plethysmography (IP) and other physiological signals are provided. The techniques limit the amplitude of MA filtered signals by imposing an “ε-tube.” The techniques may include the introduction of a regularization term to ensure that the pattern of a filtered signal is similar to the pattern of the primary component of the original, unfiltered signal by maximizing the regularity of the filtered signal. The techniques may be integrated into a portable monitoring device, such as an armband, to remove MA from various diagnostic signals and to extract primary signal components for producing enhanced device performance.

Claims

1. A computer-implemented method of adaptively filtering a signal, comprising:

sampling, by one or more processors, a signal over a plurality of successive sliding time windows, the signal including a primary signal component having an amplitude that varies over time but is substantially constant within each of the plurality of successive sliding time windows;

receiving, by one or more processors, a plurality of movement signals over a first sliding time window from among the plurality of successive sliding time windows, the plurality of movement signals being indicative of a blunt noise component that is introduced into the signal within the first sliding time window as a motion artifact due to a user'"'"'s movement, the blunt noise component having a frequency spectrum that overlaps with the received signal;

calculating, by one or more processors, a prototype signal that models dominant frequency components of the signal from a second sliding time window that chronologically precedes the first sliding time window;

calculating, by one or more processors, a set of filter coefficients utilizing the plurality of movement signals within the first sliding time window to (i) minimize an objective function associated with frequency error, and (ii) satisfy a constraint associated with the filtered signal amplitude; and

filtering, by one or more processors, the signal within the first sliding time window in accordance with the set of filter coefficients to retain the primary signal component while removing the blunt noise,

wherein the constraint is satisfied when the filtered signal amplitude within the first sliding time window is limited between a threshold value based upon the primary signal component amplitude within the second sliding time window, and

wherein the objective function is minimized when the error between frequency components of the filtered signal within the first sliding time window and the frequency components of the prototype signal are minimized.

2. The computer-implemented method of claim 1, wherein the objective function includes a term such that the amplitude of the filter coefficients are driven towards zero when no blunt noise is detected.

3. The computer-implemented method of claim 1, further comprising:

repeating the acts of receiving the plurality of movement signals, calculating the set of filter coefficients, and filtering the signal for the plurality of successive sliding time windows to perform real-time signal filtering as the signal is sampled.

4. The computer-implemented method of claim 3, wherein for each of the plurality of successive sliding time windows that are filtered, the constraint is satisfied upon the filtered signal amplitude within each of the plurality of successive sliding time windows being limited between the threshold value for each of the plurality of successive sliding time windows such that the threshold value adapts to changes in signal amplitude over time.

5. The computer-implemented method of claim 4, further comprising:

adjusting, by one or more processors, the threshold value used to determine satisfaction of the constraint for the first sliding time window when the threshold value was exceeded by the amplitude of the filtered signal within the sliding time window.

6. The computer-implemented method of claim 5, wherein the act of adjusting comprises:

adjusting the threshold value only when the blunt noise is determined to not be present in the signal.

7. The computer-implemented method of claim 5, wherein the act of calculating the prototype signal comprises:

calculating the prototype signal only when the blunt noise is determined to not be present in the signal.

8. The computer-implemented method of claim 5, wherein the act of adjusting comprises:

adjusting the threshold value to a new threshold value that is proportional to an amount in which the threshold value was exceeded by the amplitude of the primary component of the signal within the second sliding time window.

9. The computer-implemented method of claim 1, further comprising:

determining, by one or more processors, whether the blunt noise component is present within the first sliding time window based upon a power level of one or more of the plurality of movement signals exceeding a respective threshold power level.

10. The computer-implemented method of claim 9, further comprising:

calculating the set of filter coefficients and filtering the signal only when the blunt noise component is determined to be present.

11. The computer-implemented method of claim 1, wherein:

the signal corresponds to an impedance plethysmography (IP) signal,

the primary signal component corresponds to a respiratory component of the IP signal,

and

the respiratory component of the signal having an amplitude and a frequency that are substantially constant within each of the plurality of successive sliding time windows.

12. The computer-implemented method of claim 1, further comprising:

calculating, by one or more processors, a Stockwell transform of (i) the filtered signal within the first sliding time window and (ii) the prototype signal, to generate the frequency components of the filtered signal within the first sliding time window and the frequency components of the prototype signal.

13. The computer-implemented method of claim 1, wherein the act of calculating the prototype signal comprises;

calculating a piecewise linear approximation of the square of the magnitude of the frequency components of the signal within the second sliding time window.

14. The computer-implemented method of claim 1, wherein the signal includes one or more of the following:

an electrocardiogram;

an impedance cardiograph;

an impedance-based blood volume waveform;

an arterial blood pressure waveform;

a venous blood pressure waveform;

an intracranial pressure waveform;

a photoplethysmography waveform;

an end-tidal carbon dioxide waveform;

a light absorption spectral signal;

a Doppler signal; and

a piezoelectric signal.

15. The computer-implemented method of claim 1, wherein the signal is indicative of a medical diagnostic measurement, and

wherein the plurality of movement signals are indicative of a patient'"'"'s movement during the medical diagnostic measurement.

16. A data acquisition system configured to adaptively filter a medical diagnostic signal representative of a patient'"'"'s biological process, comprising:

a data acquisition module configured to (i) sample the signal over a plurality of successive sliding time windows, the signal including a respiratory signal component having an amplitude that varies within a tube that imposes a limit on the amplitude of the signal, and (ii) receive a plurality of movement signals over a first sliding time window from among the plurality of successive sliding time windows, the plurality of movement signals being indicative of a blunt noise component that is introduced into the signal within the first sliding time window as a motion artifact due to the patient'"'"'s movement, the blunt noise component having a frequency spectrum that overlaps with the received signal;

a filter coefficient calculation module configured to (i) calculate a prototype signal based upon dominant frequency components of the signal from a second sliding time window that chronologically precedes the first sliding time window, and (ii) calculate a set of filter coefficients utilizing the plurality of movement signals within the first sliding time window to minimize an objective function associated with frequency error and satisfy a constraint associated with the filtered signal amplitude; and

a filtering module configured to filter the signal within the first sliding time window in accordance with the set of filter coefficients to retain the respiratory signal component while removing the blunt noise,

wherein the constraint is satisfied when the filtered signal amplitude within the first sliding time window is limited between a threshold value based upon the primary signal component amplitude within the first sliding time window, and

wherein the objective is optimized when the error between frequency components of the filtered signal within the first sliding time window and the frequency components of the prototype signal are minimized.

17. The data acquisition system of claim 16, wherein the objective function includes a term such that the amplitude of the filter coefficients are driven towards zero when no blunt noise is detected.

18. The data acquisition system claim 16, wherein the data acquisition module, the filter coefficient calculation module, and the filtering module are further configured to receive the plurality of movement signals, to calculate the set of filter coefficients, and to filter the signal over the plurality of successive sliding time windows to perform real-time signal filtering as the signal is sampled.

19. The data acquisition system claim 18, wherein for each of the plurality of successive sliding time windows that are filtered, the constraint is satisfied upon the filtered signal amplitude within each of the plurality of successive sliding time windows being limited between the threshold value for each of the plurality of successive sliding time windows such that the threshold value adapts to changes in signal amplitude over time.

20. The data acquisition system of claim 19, wherein the filter coefficient calculation module is further configured to adjust the threshold value used to determine satisfaction of the constraint for the first sliding time window when the threshold value was exceeded by the amplitude of the primary component of the signal within the second sliding time window.

21. The data acquisition system of claim 20, wherein the filter coefficient calculation module is further configured to adjust the threshold value only when the blunt noise is determined to not be present in the signal.

22. The data acquisition system of claim 20, wherein the filter coefficient calculation module is further configured to calculate the prototype signal only when the blunt noise is determined to not be present in the signal.

23. The data acquisition system of claim 20, wherein the filter coefficient calculation module is further configured to adjust the threshold value to a new threshold value that is proportional to an amount in which the threshold value was exceeded by the amplitude of the primary component of the signal within the second sliding time window.

24. The data acquisition system claim 16, wherein the filter coefficient calculation module is further configured to determine whether the blunt noise component is present within the first sliding time window based upon a power level of one or more of the plurality of movement signals exceeding a respective threshold power level.

25. The data acquisition system of claim 24, wherein the filter coefficient calculation module is further configured to calculate the set of filter coefficients and filtering the signal only when the blunt noise component is determined to be present.

26. The data acquisition system of claim 16, wherein the signal corresponds to an impedance plethysmography (IP) signal, and

wherein the respiratory component of the IP signal has a frequency and amplitude that are substantially constant within each of the plurality of successive sliding time windows.

27. The data acquisition system of claim 16, further comprising:

a regularization module configured to calculate (i) a Stockwell transform of the filtered signal within the first sliding time window, (ii) a Stockwell transform of the prototype signal, and (iii) the frequency components of the filtered signal within the first sliding time window and the frequency components of the prototype signal based upon (i) and (ii), respectively.

28. The data acquisition system of claim 16, wherein the regularization module is further configured to calculate the prototype signal based upon a piecewise linear approximation of the square of the magnitude of the frequency components of the signal within the second sliding time window.

29. The data acquisition system of claim 16, wherein the signal includes one or more of the following:

an electrocardiogram;

an impedance cardiograph;

an impedance-based blood volume waveform;

an arterial blood pressure waveform;

a venous blood pressure waveform;

an intracranial pressure waveform;

a photoplethysmography waveform;

an end-tidal carbon dioxide waveform;

a light absorption spectral signal;

a Doppler signal; and

a piezoelectric signal.

30. The data acquisition system of claim 16, wherein the signal is indicative of a medical diagnostic measurement, and

wherein the plurality of movement signals are indicative of a patient'"'"'s movement during the medical diagnostic measurement.