Analysis and Characterization of Patient Signals

• US 20150157271A1
• Filed: 10/15/2014
• Published: 06/11/2015
• Est. Priority Date: 12/05/2013
• Status: Active Grant

• Current Assignee: PixArt Imaging Incorporated
• Original Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Inventors: Zhang, Hongxuan
• Granted Patent: US 9,848,785 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/026 Measuring blood flow A61B3/1233, A61B3/1241 take precedence
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• 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

Disclosed herein is a framework for facilitating patient signal analysis. In accordance with one aspect, first and second patient signal data are received from a patient monitor. The first and second patient signal data may then be segmented into respective first and second regions of interest, from which respective first and second waveform parameters may be extracted. One or more morphology indices may then be generated based on the first and second waveform parameters to compare shapes of the first and second regions of interest.

Claims

1. A system for patient signal analysis, comprising:

a multi-channel patient monitor configured to acquire first and second patient signal data; and

a computer system communicatively coupled to the multi-channel patient monitor, wherein the computer system includes

a non-transitory memory device for storing computer readable program code; and

a processor in communication with the memory device, the processor being operative with the computer readable program code to perform steps including

segmenting the first and second patient signal data into respective first and second regions of interest,

extracting respective first and second waveform parameters from the first and second regions of interest, and

generating one or more morphology indices based on the first and second waveform parameters to compare shapes of the first and second regions of interest.

2. The system of claim 1 wherein the first patient signal data comprises blood pressure data and the second patient signal data comprises oximetric data.

3. The system of claim 2 wherein the multi-channel patient monitor comprises a force sensing device configured to acquire the blood pressure data and a laser light-based oximetric sensor configured to acquire the oximetric data.

4. The system of claim 1 wherein the first patient signal data comprises a blood flow sound data and the second patient signal data comprises oximetric data.

5. The system of claim 4 wherein the multi-channel patient monitor comprises a vibration sensing device configured to acquire the blood flow sound data and a laser light-based oximetric sensor configured to acquire the oximetric data.

6. The system of claim 1 wherein the multi-channel patient monitor comprises multiple non-invasive sensors in switchable positions to acquire the first and second patient signal data.

7. The system of claim 1 wherein the multi-channel patient monitor comprises a wireless data transmission module configured to wirelessly communicate the first and second patient signal data to the computer system.

8. A computer-implemented method of patient signal analysis, comprising:

receiving, from a patient monitor, first and second patient signal data;

segmenting, by a processor, the first and second patient signal data into respective first and second regions of interest;

extracting, by the processor, respective first and second waveform parameters from the first and second regions of interest; and

generating, by the processor, one or more morphology indices based on the first and second waveform parameters to compare shapes of the first and second regions of interest.

9. The method of claim 8 wherein the first and second waveform parameters comprise first and second amplitude data series.

10. The method of claim 9 wherein the one or more morphology indices are generated based on the first and second amplitude data series.

11. The method of claim 9 wherein the one or more morphology indices comprise one or more mutual entropy values.

12. The method of claim 8 wherein the first and second waveform parameters comprise first and second sets of time points associated with key amplitude points.

13. The method of claim 12 wherein the key amplitude points include at least one maximum amplitude, at least one minimum amplitude and at least one local amplitude turning point.

14. The method of claim 12 wherein the one or more morphology indices comprise one or more unilateral or bilateral time duration ratios.

15. The method of claim 12 wherein the one or more morphology indices comprise one or more unilateral or bilateral time duration averaging ratios.

16. The method of claim 12 wherein the one or more morphology indices comprise one or more unilateral or bilateral time duration variability ratios.

17. The method of claim 12 wherein the one or more morphology indices comprise one or more unilateral or bilateral energy duration ratios.

18. The method of claim 12 wherein the one or more morphology indices comprise one or more unilateral or bilateral energy duration averaging ratios.

19. The method of claim 12 wherein the one or more morphology indices comprise one or more unilateral or bilateral energy variability ratios.

20. The method of claim 12 wherein the one or more morphology indices comprise one or more cross mutual time delays.

21. The method of claim 12 wherein the one or more morphology indices comprise one or more cross mutual time delay ratios.

22. The method of claim 12 wherein the one or more morphology indices comprise one or more cross mutual time delay variabilities.

23. The method of claim 8 further comprising non-linearly combining, via an artificial neural network structure, the one or more morphology indices with input patient data.

24. A non-transitory computer readable medium embodying a program of instructions executable by machine to perform steps for blood circulation characterization, the steps comprising:

receiving blood pressure data and oximetric data;

segmenting the blood pressure data and the oximetric data into respective first and second regions of interest;

extracting respective first and second waveform parameters from the first and second regions of interest; and

generating one or more morphology indices based on the first and second waveform parameters to compare shapes of the first and second regions of interest.