Physiological signal quality classification for ambulatory monitoring

• US 8,554,517 B2
• Filed: 02/25/2010
• Issued: 10/08/2013
• Est. Priority Date: 02/25/2010
• Status: Expired due to Fees

• 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:
  US12/660,458
• Publication Number:
  US 20110208009A1
• Time in Patent Office: 1,321 Days
• Field of Search: 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 for 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 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 ambulatory monitoring system peak width properties of 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 good 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 at least one physiological signal is classified as good in response to a determination that a central peak of the at least one physiological 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 at least one physiological signal has a width above a non-central peak width threshold, and wherein physiological data are extracted from the at least one physiological signal and outputted in response to classifying the at least one physiological signal as good.

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. The method of claim 1, wherein the physiological data comprise heart data.

4. The method of claim 1, wherein the physiological data comprise respiration data.

5. The method of claim 1, wherein the physiological data are outputted to a user interface whereon the physiological data are displayed.

6. The method of claim 1, wherein the physiological data are outputted to a local analysis module whereon the physiological data are subjected to higher level clinical processing.

7. The method of claim 1, wherein the physiological data are outputted to a data management element whereon the physiological data are logged.

8. The method of claim 1, wherein the physiological data are outputted to a network interface wherefrom the physiological data are transmitted remotely.