Pulse rate determination using Gaussian kernel smoothing of multiple inter-fiducial pulse periods

• US 8,870,783 B2
• Filed: 11/30/2011
• Issued: 10/28/2014
• Est. Priority Date: 11/30/2011
• Status: Active Grant

• Current Assignee: Covidien LP (Medtronic Plc)
• Original Assignee: Covidien LP (Medtronic Plc)
• Inventors: Addison, Paul S., Watson, James
• Primary Examiner(s): Cheng, Jacqueline
• Assistant Examiner(s): Hipps, Corey B
• Application Number:
  US13/307,453
• Publication Number:
  US 20130137945A1
• Time in Patent Office: 1,063 Days
• Field of Search: 600500-507, 600322-326
• US Class Current: 600/500
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence

Abstract

Systems and methods are provided for determining the pulse rate of a patient from multiple fiducial points using Gaussian kernel smoothing. Based on acquired pleth signals, each recorded fiducial pulse period is converted to a Gaussian kernel function. The Gaussian kernel functions for all recorded fiducial points are summed to generate a Gaussian kernel smoothed curve. The pulse rate of a patient may be determined from the Gaussian kernel smoothed curve. All acquired fiducial pulse periods contribute to generate the Gaussian kernel smoothing curve. The number of fiducial points utilized may change to improve pulse rate determination or provide additional functionality to the system.

Claims

1. A method for determining a physiological parameter from a physiological signal using a processor of an electronic device, comprising:

identifying at least one fiducial point on consecutive waves of the physiological signal, using the processor;

wherein the at least one fiducial point comprises a peak of the physiological signal, a trough of the physiological signal, a dicrotic notch of the physiological signal, a point of maximum slope of the physiological signal, a point of maximum curvature of the physiological signal, a pulse mean crossing of the physiological signal, a point of zero crossing in the physiological signal, or a point of zero crossing in a derivative of the physiological signal, or any combination thereof;

wherein corresponding fiducial points of the consecutive waves form sets of fiducial points;

determining a fiducial period for each set of fiducial points using the processor;

converting each of the fiducial periods to a respective Gaussian kernel function using the processor;

summing the Gaussian kernel functions to create a distribution curve using the processor;

determining the physiological parameter from the distribution curve using the processor; and

displaying on a display the determined physiological parameter.

2. The method, as set forth in claim 1, wherein identifying the at least one fiducial point comprises recording data for each fiducial point, wherein data comprises a fiducial point identifier, a time of the fiducial point, an amplitude of the fiducial point, a derivative of the physiological signal at the fiducial point, or any combination thereof.

3. The method, as set forth in claim 1, wherein identifying the at least one fiducial point comprises identifying fiducial points based on a ranking of the fiducial points.

4. The method, as set forth in claim 3, wherein the ranking of the fiducial points is based on a reliability factor of each fiducial point, a consistency factor of each fiducial point, a location factor of each fiducial point, or a quality factor of each fiducial point, or any combination thereof.

5. The method, as set forth in claim 1, wherein the steps of claim 1 are carried out for at least one prior iteration;

wherein identifying the at least one fiducial point comprises identifying fiducial points based on a ranking of the fiducial points; and

wherein the ranking of each fiducial point is dynamic and based on an effect the respective Gaussian kernel function has on the distribution curve of the at least one prior iteration.

6. The method, as set forth in claim 1, wherein identifying the at least one fiducial point comprises identifying the at least one fiducial point based on a user input number.

7. The method, as set forth in claim 1, wherein determining the physiological parameter from the distribution curve comprises determining the peak of the distribution curve.

8. The method, as set forth in claim 1, further comprising filtering the fiducial periods to remove fiducial periods corresponding to a heart rate outside the range of about 20-350 beats per minute (BPM).

9. The method, as set forth in claim 1, further comprising filtering the physiological signal to remove a non-physiological signal component.

10. The method, as set forth in claim 1, wherein a standard deviation of each Gaussian kernel function is about 0.5.

15. The method, as set forth in claim 1, wherein the at least one fiducial point is identified on consecutive waves of the physiological signal in the time domain.

16. The method, as set forth in claim 1, wherein the at least one fiducial point is identified from a derivative of the physiological signal.

17. The method, as set forth in claim 1, wherein identifying at least one fiducial point comprises identifying a plurality of different fiducial points on consecutive waves of the physiological signal.

18. The method, as set forth in claim 1, wherein the physiological signal is a plethysmographic signal.

11. A monitor comprising:

a processor configured to;

identify at least one fiducial point on consecutive waves of a physiological signal;

wherein the at least one fiducial point comprises a peak of the physiological signal, a trough of the physiological signal, a dicrotic notch of the physiological signal, a point of maximum slope of the physiological signal, a point of maximum curvature of the physiological signal, a pulse mean crossing of the physiological signal, a point of zero crossing in the physiological signal, or a point of zero crossing in a derivative of the physiological signal, or any combination thereof;

determine a fiducial period between the at least one fiducial point for each consecutive wave;

convert each of the fiducial periods to a respective Gaussian kernel function;

sum the Gaussian kernel functions to create a distribution curve;

determine a physiological parameter from the distribution curve; and

a memory configured to store data associated with each identified fiducial points.

12. The monitor, as set forth in claim 11, wherein the processor is configured to identify at least one fiducial point based on a ranking of the fiducial points, wherein the ranking of the fiducial points is based on a reliability factor of each fiducial point, a consistency factor of each fiducial point, a location factor of each fiducial point, or a quality factor of each fiducial point, or any combination thereof.

13. The monitor, as set forth in claim 12, wherein the processor is configured to determine a physiological parameter from the distribution curve created by the sum of a subset of Gaussian kernel functions.

14. The monitor, as set forth in claim 11, wherein the data stored in memory comprises a fiducial point identifier for each fiducial point, a time the fiducial point was identified, an amplitude of the identified fiducial point, or a derivative of the physiological signal at the identified fiducial point, or any combination thereof.