Physiological measurement logic engine

• US 9,211,095 B1
• Filed: 03/20/2012
• Issued: 12/15/2015
• Est. Priority Date: 10/13/2010
• Status: Active Grant

• Current Assignee: Masimo Corporation
• Original Assignee: Masimo Corporation
• Inventors: Al-Ali, Ammar
• Primary Examiner(s): WINAKUR, ERIC FRANK
• Application Number:
  US13/425,085
• Time in Patent Office: 1,365 Days
• Field of Search: 600/310, 600/322, 600/323, 600/336
• US Class Current: 1/1
• CPC Class Codes: A61B 5/0059 using light, e.g. diagnosis by transillumination, diascopy, fluorescence A61B5/0093, A61B5/1455, A61B5/02416 and A61B5/0261 take precedence
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/02108 from analysis of pulse wave characteristics
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/0261 using optical means, e.g. infrared light
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/1455 using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7271 Specific aspects of physiological measurement analysis A61B5/316, A61B5/349 and A61B5/374 take precedence
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A61B 7/02 Stethoscopes acoustic details thereof G10K11/00
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A patient monitor including a physiological measurement logic engine receives physiological data from a physiological sensor. The logic engine abstracts one or more features of the physiological data and determines a category for the abstracted feature. The logic engine further encodes the category of each of the one or more features and determines an action to perform based on the encoded categories.

Claims

1. A method for reducing processing load of a patient monitor, the method comprising:

emitting light from a light emitting element of a physiological sensor;

detecting at the physiological sensor light from the light emitting element after it has been attenuated by tissue;

receiving at a patient monitor physiological data from the physiological sensor based at least in part on the detected light;

calculating at the patient monitor in real-time a first value for each feature of one or more features of the physiological data, wherein each feature comprises a plurality of categories;

assigning the physiological data to a category of the plurality of categories for each feature based on a respective first value of the each feature;

assigning a second value to the physiological data for each feature based on the assigned category from the plurality of categories;

determining, using an electronic lookup table, an action from a set of actions to perform based on an aggregation of each second value from each of the one or more features; and

activating an alarm based at least in part on the determined action.

2. The method of claim 1, wherein the one or more features comprise at least one of a standard deviation of the physiological data, a slope of the physiological data, a change in the slope of the physiological data, a correlation coefficient of the physiological data, a linear regression of the physiological data, and a comparison of outputs between different processing engines.

3. The method of claim 1, wherein the set of actions includes at least one of changing a display of a patient monitor, changing an output of the patient monitor, receiving additional data from the physiological sensor, receiving additional data from the internet, notifying a healthcare provider, notifying the patient, powering off, and requesting additional information from a user.

4. The method of claim 1, wherein the one or more features are predetermined.

5. The method of claim 1, further comprising selecting the one or more features from a predetermined set of features.

6. The method of claim 5, wherein selecting the one or more features comprises selecting all the features on the list.

7. The method of claim 5, wherein selecting the one or more features comprises selecting a subset of the features on the list.

8. The method of claim 5, wherein selecting the one or more features comprises selecting the one or more features that use fewer processing resources.

9. The method of claim 5, wherein selecting the one or more features comprises selecting the one or more features based on a type of physiological data obtained.

10. The method of claim 1, wherein the physiological sensor is one of a pulse oximeter sensor and an acoustic sensor.

11. A patient monitor system comprising:

a physiological sensor configured to emit light towards a tissue site, detect the light after it has been attenuated by tissue, and obtain physiological data based at least in part on the detected light; and

a patient monitor in communication with the physiological sensor and configured to;

receive the physiological data from the physiological sensor;

determine one or more features of the physiological data;

determine a category for the physiological data within each of the one or more features of the physiological data;

associate an action with the physiological data based on the one or more determined categories using an electronic lookup table, wherein associating the action with the physiological data is based on an aggregation of the determined categories when there are multiple determined categories; and

activate an alarm based at least in part on the determined action.

12. The patient monitor system of claim 11, wherein the patient monitor determines in real-time the one or more features of the physiological data.

13. The patient monitor system of claim 11, wherein the one or more features comprise at least one of a standard deviation of the physiological data, a slope of the physiological data, a change in the slope of the physiological data, and a comparison of outputs between different processing engines.

14. The patient monitor system of claim 11, wherein the one or more features are predetermined.

15. The patient monitor system of claim 11, wherein the patient monitor is further configured to select the one or more features from a predetermined list of features, wherein the patient monitor determines the one or more features selected from the predetermined list of features.

16. The patient monitor system of claim 15, wherein the patient monitor selects the one or more features by selecting all the features on the list.

17. The patient monitor system of claim 15, wherein patient monitor selects the one or more features by selecting a subset of the features on the list.

18. The patient monitor system of claim 15, wherein patient monitor selects the one or more features by selecting the one or more features that use fewer processing resources.

19. The patient monitor system of claim 15, wherein patient monitor selects the one or more features by selecting the one or more features based on a type of physiological data obtained.

20. The patient monitor system of claim 11, wherein the physiological sensor is one of a pulse oximeter sensor and an acoustic sensor.

21. A computer-implemented method for reducing processing load of a patient monitor, the method comprising:

under the control of one or more computing devices of a patient monitor;

receiving physiological data from a physiological sensor configured to emit light towards a tissue site, detect the light after it has been attenuated by tissue, and obtain the physiological data based at least in part on the detected light;

determining one or more features of the physiological data;

determining a category for the physiological data within each of the one or more features of the physiological data;

associating an action with the physiological data based on the one or more determined categories, wherein associating the action with the physiological data is based on an aggregation of the determined categories when there are multiple determined categories; and

activating an alarm based at least in part on the determined action.

22. The method of claim 21, wherein the action associated with the physiological data is based on the output of an electronic lookup table stored in a computer-readable medium.

23. The method of claim 21, wherein the determining one or more features of the physiological data occurs in real-time.