Non-invasive determination of disease states

• US 10,624,587 B2
• Filed: 09/15/2017
• Issued: 04/21/2020
• Est. Priority Date: 09/16/2016
• Status: Active Grant

• Current Assignee: Welch Allyn Incorporated
• Original Assignee: Welch Allyn Incorporated
• Inventors: Baker, Steven D., Ye, Chau Chong, Wang, Yue, Aung, Aye
• Primary Examiner(s): Alter, Alyssa M
• Application Number:
  US15/705,986
• Publication Number:
  US 20180078216A1
• Time in Patent Office: 949 Days
• CPC Class Codes: A61B 2576/00 Medical imaging apparatus involving image processing or analysis A61B1/00009, A61B6/52 and A61B8/52 take precedence; image processing per se G06T; details of algorithms for analysing medical images G06T7/0012
• CPC Class Codes: A61B 3/0025 characterised by electronic signal processing, e.g. eye models
• CPC Class Codes: A61B 3/112 for measuring diameter of pupils
• CPC Class Codes: A61B 3/14 Arrangements specially adapted for eye photography apparatus or arrangements for taking photographs per se G03B
• CPC Class Codes: A61B 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• 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 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/02444 Details of sensor A61B5/02427 takes precedence
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/1079 using optical or photographic means
• CPC Class Codes: A61B 5/1107 Measuring contraction of parts of the body, e.g. organ, muscle apparatus for measuring work or force in general G01L
• CPC Class Codes: A61B 5/1116 Determining posture transitions
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/1128 using image analysis A61B5/1127 takes precedence
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence

Abstract

An example method for estimating a disease state for a patient can include: capturing physiological data including images from the patient over time; processing the physiological data to detect a variability; comparing the variability to a baseline or a range; and estimating the disease state for the patient based upon the comparing of the variability to the baseline or the limit.

Claims

1. A method for estimating a disease state for a patient, the method comprising:

capturing images of an unobstructed portion of the patient;

processing the images to detect a pulse rate;

determining a pulse rate variability based upon the pulse rate; and

calculating an early warning score using the pulse rate variability, including;

comparing the pulse rate variability to a baseline having similar demographics; and

increasing or decreasing the early warning score based upon a variance from the baseline.

2. The method of claim 1, further comprising transmitting pulse rate variability to an electronic medical record.

3. The method of claim 2, further comprising annotating data with at least one patient outcome metric.

4. The method of claim 3, further comprising using one or more annotations to measure algorithm performance.

5. The method of claim 1, wherein estimating the disease state further comprises analyzing patient health factors.

6. The method of claim 1, further comprising processing the images to detect a respiratory rate for the patient.

7. The method of claim 1, further comprising forming an autoregressive model using data from the images to estimate the pulse rate of the patient.

8. The method of claim 7, further comprising combining the autoregressive model estimate of the pulse rate and pulse-rate estimates derived from analysis of motion data.

9. The method of claim 1, further comprising providing a trend of the pulse rate variability for the patient over time.