Physiological signal quality classification for ambulatory monitoring

• US 8,949,077 B2
• Filed: 09/20/2013
• Issued: 02/03/2015
• 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: Fu, Yongji, Yang, Te-Chung Isaac
• Primary Examiner(s): Le, John H
• Application Number:
  US14/032,401
• Publication Number:
  US 20140025311A1
• Time in Patent Office: 501 Days
• Field of Search: 702/19, 702/69, 702/71, 702/189, 702/190, 702/193, 600/300, 600/301, 600/508, 600/509, 600/524, 600/529, 600/544
• US Class Current: 702/189
• 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:

detecting by the ambulatory monitoring system, using a sound transducer, physiological sounds;

generating by the ambulatory monitoring system, using the sound transducer, physiological signals representing the physiological sounds;

comparing by the ambulatory monitoring system peak width properties of the physiological signals with distinguishing peak width properties of good, noisy and weak signals;

classifying by the ambulatory monitoring system at least one of the physiological signals as noisy based on at least one of the comparisons; and

processing by the ambulatory monitoring system the at least one physiological signal in accordance with the classification, wherein the physiological signal is classified as noisy based at least in part on a determination that a central peak of the physiological signal has a width above a central peak width threshold, and wherein a noisy signal notification is generated and outputted in response to classifying the physiological signal as noisy.

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

extracting by the ambulatory monitoring system envelopes for the physiological signals; and

generating by the ambulatory monitoring system using the envelopes autocorrelation results for the physiological signals, wherein the comparisons are between peak width properties of the autocorrelation results and distinguishing peak width properties of good, noisy and weak signals.

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

detecting by the ambulatory monitoring system, using a sound transducer, physiological sound;

generating by the ambulatory monitoring system, using the sound transducer, physiological signals representing the physiological sounds;

comparing by the ambulatory monitoring system peak width properties of the physiological signals with distinguishing peak width properties of good, noisy and weak signals;

classifying by the ambulatory monitoring system at least one of the physiological signals as weak based on at least one of the comparisons; and

processing by the ambulatory monitoring system the at least one physiological signal in accordance with the classification, wherein the physiological signal is classified as weak based at least in part on a determination that a peak of the physiological signal has a width below a width threshold, and wherein a weak signal notification is generated and outputted in response to classifying the physiological signal as weak.

4. The method of claim 3, further comprising the steps of

extracting by the ambulatory monitoring system envelopes for the physiological signals;

and

generating by the ambulatory monitoring system using the envelopes autocorrelation results for the physiological signals, wherein the comparisons are between peak width properties of the autocorrelation results and distinguishing peak width properties of good, noisy and weak signals.

5. The method of claim 3, wherein the at least one physiological signal is classified as weak based at least in part on a determination that a central peak of the physiological signal has a width below a central peak width threshold.

6. The method of claim 3, wherein the at least one physiological signal is classified as weak based at least in part on a determination that a non-central peak of the physiological signal has a width below a non-central peak width threshold.