Respiration Monitoring Method and Device with Context-Aware Event Classification

• US 20160120479A1
• Filed: 10/31/2014
• Published: 05/05/2016
• Est. Priority Date: 10/31/2014
• Status: Abandoned Application

• Current Assignee: Sharp Laboratories of America Incorporated (Hon Hai Precision Industry Co., Ltd.)
• Original Assignee: Sharp Laboratories of America Incorporated (Hon Hai Precision Industry Co., Ltd.)
• Inventors: Hallberg, Bryan Severt
• Application Number:
  US14/529,949
• Publication Number:
  US 20160120479A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2562/0204 Acoustic sensors
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/0836 Measuring rate of CO2 production
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• 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 5/7282 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 7/003 Detecting lung or respiration noise
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A respiration monitoring method and device with context-aware noise detection use context supplied by neighboring events when deciding how to classify a target event. More particularly, the present invention considers similarities between a target event and neighboring events relative to event attributes and timing to inform the decision whether to classify the target event as a respiration event or a noise event. Target events classified as noise events are removed from the respiration signal or otherwise ignored when estimating respiration parameters.

Claims

1. A respiration monitoring device, comprising:

a body sensor configured to sense respiration activity and generate a respiration signal representing the respiration activity;

an event identification engine configured to identify candidate respiration events in the respiration signal;

an event classification engine configured to select individual target events from the candidate respiration events, compute an attribute similarity metric for each of the target events by comparing a target value of an attribute of the target event with a reference value for the attribute determined from one or more events neighboring the target event, and classify the target event as one of a noise event or a respiration event using the attribute similarity metric;

a respiration parameter estimator configured to compute a respiration parameter estimate using the target events classified as respiration events; and

a respiration data output interface configured to output respiration data using the respiration parameter estimate.

2. The device of claim 1, wherein the attribute similarity metric is computed as a quotient of the target value and the reference value.

3. The device of claim 1, wherein the target event is classified using a result of a comparison of the attribute similarity metric with a threshold value.

4. The device of claim 1, wherein the target event is classified using a result of a comparison of the attribute similarity metric and a timing similarity metric with a threshold value.

5. The device of claim 1, wherein the reference value is selected as a value of the attribute for one of the neighboring events which is most similar to the values of the attribute for the other neighboring events.

6. The device of claim 1, wherein the attribute is mean event amplitude.

7. The device of claim 1, wherein the attribute is event width.

8. The device of claim 1, wherein the attribute is event area.

9. The device of claim 1, wherein the timing similarity metric is computed using a period and phase interval associated with the target event.

10. The device of claim 1, wherein the body sensor is a sound sensor.

11. The device of claim 1, wherein the respiration parameter is respiration rate.

12. The device of claim 1, wherein the event classification engine is further configured to be able to classify at least one of the candidate respiration events as a noise event using a result of a comparison of a value of an attribute of the candidate respiration event with a threshold value of the attribute.

13. A respiration monitoring method for a respiration monitoring device having a body sensor, comprising:

sensing, by the body sensor, respiration activity;

generating, by the device, a respiration signal representing the respiration activity;

identifying, by the device, candidate respiration events in the respiration signal;

selecting, by the device, individual target events from the candidate respiration events;

computing, by the device, an attribute similarity metric for each of the target events by comparing a target value of an attribute of the target event with a reference value of the attribute determined from one or more events neighboring the target event;

classifying, by the device, the target event as one of a noise event or a respiration event using the attribute similarity metric;

computing, by the device, a respiration parameter estimate using the target events classified as respiration events; and

outputting, by the device, respiration data using the respiration parameter estimate.

14. The method of claim 13, wherein the attribute similarity metric is computed as a quotient of the target value and the reference value.

15. The method of claim 13, wherein the target event is classified using a result of a comparison of the attribute similarity metric with a threshold value.

16. The method of claim 13, wherein the target event is classified using a result of a comparison of the attribute similarity metric and a timing similarity metric with a threshold value.

17. The method of claim 13, wherein the reference value is selected as a value of the attribute for one of the neighboring events which is most similar to the values of the attribute for the other neighboring events.

18. The method of claim 13, wherein the attribute is one of mean event amplitude, event width or event area.

19. The method of claim 13, wherein the timing similarity metric is computed using a period and phase interval associated with the target event.

20. The method of claim 13, wherein the body sensor is a sound sensor.