METHODS AND DEVICES FOR DISPLAYING TREND AND VARIABILITY IN A PHYSIOLOGICAL DATASET

• US 20170213368A1
• Filed: 03/09/2017
• Published: 07/27/2017
• 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: Lalli, Michael, Bock, Malcolm, Freeman, Jenny, MacNabb, Colin M., Brayanov, Jordan
• Granted Patent: US 10,347,020 B2
• Time in Patent Office: Days
• 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 graph of the trend of the physiological dataset; and

outputting a graph of the variability of the physiological dataset.

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 trend of the physiological dataset and the graph of the variability of the physiological dataset are one of overlaid and graphed adjacently.

5. The method of claim 1, wherein the graph of the variability of the physiological dataset comprises an 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.

6. The method of claim 5, 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 ; and

an output device coupled to the processor and adapted to;

output a graph of the trend of the physiological dataset; and

output a graph of the variability of the physiological dataset.

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;

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 the variability of the physiological dataset;

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

outputs a graph of the variability of the physiological dataset to the screen.

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 trend of the physiological dataset and the graph of the variability of the physiological dataset are one of overlaid and graphed adjacently.

17. The system of claim 13, wherein the graph of the variability of the physiological dataset comprises an 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.

18. The system of claim 17, 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.