Physiological signal quality classification for ambulatory monitoring

• US 20110208009A1
• Filed: 02/25/2010
• Published: 08/25/2011
• Est. Priority Date: 02/25/2010
• Status: Active Grant

• Current Assignee: Sharp Kabushiki Kaisha (Hon Hai Precision Industry Co., Ltd.)
• Original Assignee: Sharp Laboratories of America Incorporated (Hon Hai Precision Industry Co., Ltd.)
• Inventors: Yang, Te-Chung Isaac, Fu, Yongji
• Granted Patent: US 8,554,517 B2
• 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 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/02444 Details of sensor A61B5/02427 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 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 7/04 Electric stethoscopes microphones, acoustic transducers therefor H04R
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations

Abstract

Physiological signal quality classification methods and systems designed to improve ambulatory monitoring. Physiological signals are classified as good, noisy or weak based on signal properties. Once classified, signals are processed differently depending on their classification in order to encourage reliance on reliable physiological data, discourage reliance on unreliable physiological data and induce action to improve signal quality. For example, for a good signal, physiological data may be extracted from the signal and displayed to a person being monitored. For a noisy signal, a noisy signal notification may be displayed to the person in lieu of extracted physiological data. For a weak signal, a weak signal notification may be displayed to the person in lieu of extracted physiological data. Moreover, a noisy or weak signal notification displayed to a person being monitored may be accompanied by a corrective action recommendation, such as “move to quieter environment” for a noisy signal or “check body placement of sensor” for a weak signal.

Claims

1. A physiological signal processing method for an ambulatory monitoring system, comprising the steps of:

comparing by the system one or more properties of a physiological signal with one or more distinguishing properties of good, noisy and weak signals;

classifying by the system the signal as one of good, noisy or weak based on the comparison; and

processing by the system the signal in accordance with the classification.

2. The method of claim 1, further comprising the steps of:

extracting by the system an envelope for the signal; and

generating by the system using the envelope an autocorrelation result for the signal, wherein the comparison is between one or more properties of the autocorrelation result and one or more distinguishing properties of good, noisy and weak signals.

3. The method of claim 1, wherein the distinguishing properties comprise central peak width.

4. The method of claim 1, wherein the distinguishing properties comprise non-central peak width.

5. The method of claim 1, wherein the classifying step comprises classifying the signal as good based at least in part on a determination that a central peak of the signal has a width between a low central peak width threshold and a high central peak width threshold and a determination that a non-central peak of the signal has a width above a non-central peak width threshold.

6. The method of claim 1, wherein the classifying step comprises classifying the signal as noisy based at least in part on a determination that a central peak of the signal has a width above a high central peak width threshold.

7. The method of claim 1, wherein the classifying step comprises classifying the signal as weak based at least in part on a determination that a central peak of the signal has a width below a low central peak width threshold.

8. The method of claim 1, wherein the classifying step comprises classifying the signal as weak based at least in part on a determination that a non-central peak of the signal has a width below a non-central peak width threshold.

9. The method of claim 1, wherein the processing step comprises extracting physiological data from the signal in response to classifying the signal as good, and outputting the physiological data.

10. The method of claim 1, wherein the processing step comprises generating a noisy signal notification in response to classifying the signal as noisy, and outputting the noisy signal notification.

11. The method of claim 1, wherein the processing step comprises generating a weak signal notification in response to classifying the signal as weak, and outputting the weak signal notification.

12. An ambulatory monitoring system, comprising:

a transducer; and

a signal processor communicatively coupled with the transducer, wherein under control of the signal processor the system compares one or more properties of a physiological signal detected by the transducer with one or more distinguishing properties of good, noisy and weak signals, classifies the signal as one of good, noisy or weak based on the comparison, and processes the signal in accordance with the classification.

13. The system of claim 12, wherein under control of the signal processor the system extracts an envelope for the signal, and generates using the envelope an autocorrelation result for the signal, wherein the comparison is between one or more properties of the autocorrelation result and one or more distinguishing properties of good, noisy and weak signals.

14. The system of claim 12, wherein under control of the signal processor the system classifies the signal as good based at least in part on a determination that a central peak of the signal has a width between a low central peak width threshold and a high central peak width threshold and a determination that a non-central peak of the signal has a width above a non-central peak width threshold.

15. The system of claim 12, wherein under control of the signal processor the system classifies the signal as noisy based at least in part on a determination that a central peak of the signal has a width above a high central peak width threshold.

16. The system of claim 12, wherein under control of the signal processor the system classifies the signal as weak based at least in part on a determination that a central peak of the signal has a width below a low central peak width threshold.

17. The system of claim 12, wherein under control of the signal processor the system classifies the signal as weak based at least in part on a determination that a non-central peak of the signal has a width below a non-central peak width threshold.

18. An ambulatory monitoring system, comprising:

a transducer; and

a signal processor communicatively coupled with the transducer, wherein under control of the signal processor the system compares signal peak widths of an autocorrelation result of a physiological signal generated by the transducer with predetermined signal peak width thresholds distinguishing a plurality of signal types, classifies the signal into one of the signal types based on the comparison, and processes the signal in accordance with the classification.

19. The system of claim 18, wherein the plurality of signal types comprise at least two of good, noisy and weak.

20. The system of claim 18, wherein the signal peak widths of the autocorrelation result comprise a central peak width and a non-central peak width.