Linear classification method for determining acoustic physiological signal quality and device for use therein

• US 20120029298A1
• Filed: 07/28/2010
• Published: 02/02/2012
• Est. Priority Date: 07/28/2010
• 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: Fu, Yongji, Yang, Te-Chung Isaac, Hallberg, Bryan Severt
• Application Number:
  US12/804,749
• Publication Number:
  US 20120029298A1
• Time in Patent Office: Days
• US Class Current: 600/300
• 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 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• 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 7/00 Instruments for auscultation
• CPC Class Codes: G10L 25/48 specially adapted for particular use
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Linear classification is used to determine the quality of acoustic physiological signal samples. A feature dataset is extracted from acoustic physiological signal samples of known quality (i.e., weak, noisy, good) acquired over a sampling period. A linear discriminant analysis is performed on the feature dataset to determine a direction of a linear classifier for the feature dataset. A classification error risk analysis is performed on the feature dataset to determine an offset of the linear classifier. The linear classifier is used to classify into reliability classes acoustic physiological signal samples acquired over an operating period. Information is selected for outputting using the assigned classifications, and is outputted.

Claims

1. A method for using linear classification to determine the quality of acoustic physiological signal samples, comprising the steps of:

extracting a feature dataset from first acoustic physiological signal samples of predetermined reliability;

determining a linear classifier from the feature dataset;

assigning to reliability classes second acoustic physiological signal samples acquired by a physiological monitoring device using the linear classifier; and

outputting by the physiological monitoring device information selected using the assigned reliability classes.

2. The method of claim 1, wherein the feature dataset comprises central peak width data for autocorrelation results generated from energy envelopes extracted from the first acoustic physiological signal samples.

3. The method of claim 1, wherein the feature dataset comprises non-central peak amplitude data for autocorrelation results generated from energy envelopes extracted from the first acoustic physiological signal samples.

4. The method of claim 1, wherein the step of determining a linear classifier comprises determining a direction of the linear classifier using a linear discriminant analysis (LDA).

5. The method of claim 4, wherein the LDA invokes the Fisher method.

6. The method of claim 1, wherein the step of determining a linear classifier comprises determining an offset of the linear classifier using a classification error risk analysis.

7. The method of claim 1, wherein the information comprises a confidence level.

8. The method of claim 1, wherein the information comprises a reliability indicator.

9. The method of claim 1, wherein the information comprises a recommendation as to how to improve reliability.

10. The method of claim 1, wherein the information is displayed on the physiological monitoring device.

11. The method of claim 1, wherein the extracting and determining steps are performed by the physiological monitoring device.

12. The method of claim 1, wherein the physiological monitoring device is portable.

13. A physiological monitoring device, comprising:

a physiological data capture system;

a physiological data processing system communicatively coupled with the capture system; and

a physiological data output interface communicatively coupled with the processing system, wherein under control of the processing system the device assigns to reliability classes using a linear classifier acoustic physiological signal samples acquired by the device and selects using the assigned reliability classes information respecting the acoustic physiological signal samples, and wherein the information is outputted on the output interface.

14. The device of claim 13, wherein under control of the processing system the device determines the linear classifier from a feature dataset extracted from acoustic physiological signal samples of predetermined reliability.

15. The device of claim 14, wherein the feature dataset comprises central peak width data for autocorrelation results generated from energy envelopes extracted from the first acoustic physiological signal samples.

16. The device of claim 14, wherein the feature dataset comprises non-central peak amplitude data for autocorrelation results generated from energy envelopes extracted from the first acoustic physiological signal samples.

17. The device of claim 13, wherein a direction of the linear classifier is determined using a LDA.

18. The device of claim 13, wherein an offset of the linear classifier is determined using a classification error risk analysis.

19. The device of claim 13, wherein the information is displayed on the output interface.

20. The device of claim 13, wherein the device is portable.