SYSTEM AND METHOD FOR AUTOMATIC DETECTION OF A PLURALITY OF SPO2 TIME SERIES PATTERN TYPES

• US 20130060110A1
• Filed: 09/05/2012
• Published: 03/07/2013
• Est. Priority Date: 01/27/1997
• Status: Abandoned Application

• Current Assignee: Lawrence A. Lynn
• Original Assignee: Lawrence A. Lynn
• Inventors: Lynn, Lawrence A., Lynn, Eric N.
• Application Number:
  US13/603,573
• Publication Number:
  US 20130060110A1
• Time in Patent Office: Days
• US Class Current: 600/324
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• 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 5/0836 Measuring rate of CO2 production
• CPC Class Codes: A61B 5/087 Measuring breath flow
• CPC Class Codes: A61B 5/091 Measuring volume of inspired or expired gases, e.g. to determine lung capacity
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/412 Detecting or monitoring sepsis
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/743 Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
• CPC Class Codes: A61B 7/003 Detecting lung or respiration noise
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 20/40 relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

The disclosed embodiments relate to pulse oximetry. An exemplary pulse oximeter comprises a probe that is adapted to be attached to a body part of a patient to create a signal indicative of an oxygen saturation of blood of the patient, and a processor that is adapted to receive the signal produced by the probe, to calculate an SPO2 value based on the signal, to detect a plurality of pattern types of SPO2 indicative of pathophysiologic events, and to produce an output indicative of a detected one of the plurality of pattern types.

Claims

1. A monitoring system, comprising:

a monitor capable of receiving input relating to patient SPO2 or CO2 physiological parameters and storing historical data related to the parameters;

a display feature capable of displaying the historical data corresponding to the patient physiological parameters;

a pattern detection feature capable of analyzing the historical data to detect a physiologic cycling pattern of SPO2 or CO2 in a segment of the historical data wherein the cycling corresponds to at least one of the types of cycling patterns induced by sleep apnea; and

a display control feature capable of finding and displaying the cycling pattern in the historical data on the screen when the display control feature is activated.

2. The system of claim 1, wherein the display control feature is capable of automatically finding and displaying user-specified types of events.

3. The system of claim 1, wherein the historical data comprises a trend of pulse oximetry data and the physiologic pattern comprises a saturation pattern.

4. The system of claim 1, wherein the historical data comprises a trend of pulse oximetry data and the physiologic patter comprises a pattern indicative of ventilatory instability and/or sleep apnea.

5. The system of claim 1, comprising a feature capable of determining a scoring metric associated with the detected physiologic pattern.

6. The system of claim 1, comprising a dynamic status indicator capable of indicating a severity of a clustering of detected saturation.

7. The system of claim 6, wherein the dynamic status indicator comprises bars capable of increasing in number as a severity of the clustering increases based on the historical data.

8. A method, comprising:

receiving physiological data from a SPO2 sensor;

identifying a plurality of events in the physiological data;

determining whether a physiologic pattern of SPO2 or CO2 cycling wherein the cycling corresponds to at least one of the types of cycling patterns induced by sleep apnea, is present in the physiological data based on whether the plurality of events meet defined criteria;

displaying the physiological data as a trend; and

graphically designating a segment of the trend with a graphic indicator that corresponds to an identified SPO2 cycling pattern.

9. The method of claim 8, wherein the graphic indicator comprises identifying the segment of the trend.