Methods and devices for displaying trend and variability in a physiological dataset

• US 10,347,020 B2
• Filed: 03/09/2017
• Issued: 07/09/2019
• Est. Priority Date: 03/06/2014
• Status: Active Grant

• Current Assignee: Respiratory Motion, Inc. (Senzime AB (publ))
• Original Assignee: Respiratory Motion, Inc. (Senzime AB (publ))
• Inventors: Brayanov, Jordan, Bock, Malcolm, Lalli, Michael, Freeman, Jenny, MacNabb, Colin M.
• Primary Examiner(s): Amini, Javid A
• Application Number:
  US15/454,500
• Publication Number:
  US 20170213368A1
• Time in Patent Office: 852 Days
• Field of Search: 345440
• CPC Class Codes: G06T 11/206 Drawing of charts or graphs
• CPC Class Codes: G06T 11/60 Editing figures and text; Combining figures or text
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Embodiments of the invention are directed to methods and devices for displaying trends and variability in a physiological dataset. The method comprises obtaining the physiological dataset, applying a smoothing algorithm to the physiological dataset to obtain a trend of the physiological dataset, applying a variability algorithm to the physiological dataset to obtain the variability of the physiological dataset, outputting a graph of the trend of the physiological dataset, and outputting a graph of the variability of the physiological dataset.

Claims

1. A method of displaying trends and variability in a physiological dataset, comprising, on a processor:

obtaining the physiological dataset;

applying a smoothing algorithm to the physiological dataset to obtain a trend of the physiological dataset;

applying a variability algorithm to the physiological dataset to obtain the variability of the physiological dataset;

outputting a real-time graph of the trend of the physiological dataset;

outputting a real-time graph of the variability of the physiological dataset overlaid on the graph of the trend of the physiological dataset; and

triggering at least one of an audible or visual alarm upon a predetermined condition in the variability or trend of the physiological dataset being met whereas the predetermined condition is based on at least one of;

(1) a ratio between variability and trend values within a time window, (2) a difference between a maximum and a minimum within a time window, (3) a value of the trend within a time window, (4) a change in the trend values from one window to a next window, or (5) a change in a difference between maximum and minimum within a time window from one window to the next window.

2. The method of claim 1, wherein the physiological dataset is based on data obtained from a patient'"'"'s respiratory system.

3. The method of claim 1, wherein the smoothing algorithm is one of a moving average algorithm and a digital filter algorithm.

4. The method of claim 1, wherein the graph of the variability of the physiological dataset further comprises a real time visual envelope bounded on the top by a plot of the maximums identified by the variability algorithm and bounded on the bottom by a plot of the minimums identified by the variability algorithm and the at least one of an audible or visual alarm is triggered upon an area within the maximum envelope and the minimum envelope reaching a predetermined level.

5. The method of claim 4, wherein where said maximums and minimums are identified within each time window over which the variability is estimated.

6. The method of claim 4, wherein the space between the bounds is shaded.

7. The method of claim 6, wherein the graph of the variability of the physiological dataset is used to assess and diagnose apnea.

8. The method of claim 1, wherein the physiological dataset is interbreath interval data.

9. The method of claim 1, wherein the graph of variability of the physiological dataset is a function of fractal scaling coefficients calculated at various time points and over various time windows of the dataset.

10. The method of claim 1, wherein the graph of variability of the physiological dataset comprises one or more of, error bars, line graphs, momentum bars, shaded areas under a curve, and a stochastic plot.

11. The method of claim 1, wherein the magnitude of the variability which is displayed by the graph of variability of the physiological dataset is calculated as a function of at least one of, the raw dataset, the smoothed dataset, multiple smoothed datasets, the fractal scaling coefficients of the dataset, or the stochastic coefficients of the dataset.

12. A device comprising:

a transthoracic impedance measurement device to obtain a physiological dataset;

a processor receiving the physiological dataset from the measurement device, and adapted to;

apply a smoothing algorithm to the physiological dataset to obtain a trend of the physiological dataset; and

apply a variability algorithm to the physiological dataset to obtain the variability of the physiological dataset;

an output device coupled to the processor and adapted to;

output a real-time graph of the trend of the physiological dataset; and

output a real-time graph of the variability of the physiological dataset overlaid on the graph of the trend of the physiological dataset; and

at least one of an audible or visual alarm triggered upon a predetermined condition in the variability or trend of the physiological dataset being met whereas the predetermined condition is based on at least one of;

(1) a ratio between variability and trend values within a time window, (2) a difference between a maximum and a minimum within a time window, (3) a value of the trend within a time window, (4) a change in the trend values from one window to a next window, or (5) a change in a difference between maximum and minimum within a time window from one window to the next window.

13. A system for displaying trends and variability in a physiological dataset, comprising:

a patient monitoring device;

at least one sensor coupled to the patient monitoring device;

a processor contained within the patient monitoring device and receiving patient data from the at least on sensor;

a screen contained within the patient monitoring device and receiving display information from the processor; and

at least one of an audible or visual alarm triggered upon a predetermined condition in the variability or trend of the physiological dataset being met whereas the predetermined condition is based on at least one of;

(1) a ratio between variability and trend values within a time window, (2) a difference between a maximum and a minimum within a time window, (3) a value of the trend within a time window, (4) a change in the trend values from one window to a next window, or (5) a change in a difference between maximum and minimum within a time window from one window to the next window;

wherein the processor;

obtains the physiological dataset from the at least one sensor;

applies a smoothing algorithm to the physiological dataset to obtain a trend of the physiological dataset;

applies a variability algorithm to the physiological dataset to obtain a variability of the physiological dataset;

outputs a real-time graph of the trend of the physiological dataset to the screen; and

outputs a real-time graph of the variability of the physiological dataset to the screen overlaid on the graph of the trend of the physiological dataset.

14. The system of claim 13, wherein the physiological dataset is based on data obtained from a patient'"'"'s respiratory system.

15. The system of claim 13, wherein the smoothing algorithm is one of a moving average algorithm and a digital filter algorithm.

16. The system of claim 13, wherein the graph of the variability of the physiological dataset further comprises a real time visual envelope bounded on the top by a plot of the maximums identified by the variability algorithm and bounded on the bottom by a plot of the minimums identified by the variability algorithm and the at least one of an audible or visual alarm is triggered upon an area within the maximum envelope and the minimum envelope reaching a predetermined level.

17. The system of claim 16, wherein where said maximums and minimums are identified within each time window over which the variability is estimated.

18. The system of claim 16, wherein the space between the bounds is shaded.

19. The system of claim 18, wherein the graph of the variability of the physiological dataset is used to assess and diagnose apnea.

20. The system of claim 13, wherein the physiological dataset is interbreath interval data.

21. The system of claim 13, wherein the graph of variability of the physiological dataset is a function of fractal scaling coefficients calculated at various time points and over various time windows of the dataset.

22. The system of claim 13, wherein the graph of variability of the physiological dataset comprises one or more of, error bars, line graphs, momentum bars, shaded areas under a curve, and a stochastic plot.

23. The system of claim 13, wherein the magnitude of the variability which is displayed by the graph of variability of the physiological dataset is calculated as a function of at least one of, the raw dataset, the smoothed dataset, multiple smoothed datasets, the fractal scaling coefficients of the dataset, or the stochastic coefficients of the dataset.