DETERMINATION OF A PHYSIOLOGICAL PARAMETER

• US 20140378795A1
• Filed: 09/08/2014
• Published: 12/25/2014
• Est. Priority Date: 09/24/2009
• Status: Abandoned Application

• Current Assignee: Covidien LP (Medtronic Plc)
• Original Assignee: Covidien LP (Medtronic Plc)
• Inventors: McKenna, Edward M.
• Application Number:
  US14/480,190
• Publication Number:
  US 20140378795A1
• Time in Patent Office: Days
• US Class Current: 600/323
• CPC Class Codes: A61B 5/0015 characterised by features of the telemetry system
• CPC Class Codes: A61B 5/1455 using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: G06F 16/23 Updating
• CPC Class Codes: G06F 16/2455 Query execution
• CPC Class Codes: G06F 2218/18 by plotting the signal segments against each other, e.g. analysing scattergrams
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/10 Signal processing, e.g. from mass spectrometry [MS] or from PCR
• CPC Class Codes: G16B 40/20 Supervised data analysis
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 40/40 for the management of medical equipment or devices, e.g. scheduling maintenance or upgrades
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Methods and systems are provided for analyzing a physiological signal by applying a continuous wavelet transform on the signal and comparing the wavelet transformation to a library of wavelet signatures corresponding to one or more physiological conditions and/or patient conditions. A pulse oximeter system may relate the wavelet transformation with one or more of the wavelet signatures based on filters and/or thresholds, and may determine that the wavelet transformation indicates that the patient of the physiological signal has a physiological condition indicated by the related wavelet signature. In some embodiments, the pulse oximeter system may use previous analyses in a neural network to update the library. Further, non-physiological components of the wavelet transformation may also be identified and removed.

Claims

1. A method for generating a data library, comprising:

acquiring a plurality of physiological signals associated with a physiological condition of a patient;

performing a continuous wavelet transform on each physiological signal to generate a respective wavelet transform product for each physiological signal; and

organizing the wavelet transform products so that the wavelet transform products may be searched using a current wavelet transform product derived from a current physiological signal.

2. The method, as set forth in claim 1, wherein acquiring the plurality of physiological signals comprises acquiring the wavelet transform products.

3. The method, as set forth in claim 1, comprising saving the wavelet transform products to the data library.

4. The method, as set forth in claim 1, wherein the physiological condition of the patient comprises one or more of patient physiological data, recommendations, diagnoses, and patient characteristics.

5. The method, as set forth in claim 1, comprising organizing the wavelet transform products based on one or more of patient physiological data, recommendations, diagnoses, and patient characteristics.

6. The method, as set forth in claim 1, comprising acquiring the plurality of physiological signals from a pulse oximeter sensor of a pulse oximeter system.

7. The method, as set forth in claim 6, comprising organizing the wavelet transform products based on characteristics of the pulse oximeter system.

8. The method, as set forth in claim 6, wherein the pulse oximeter sensor comprises an encoder configured to transmit information indicative of a type of the pulse oximeter sensor to a processor that is configured to organize the wavelet transform products based on the type of the pulse oximeter sensor.

9. The method, as set forth in claim 1, comprising utilizing a supervised learning technique to update the wavelet transform products in the data library with the current wavelet transform product.

10. The method, as set forth in claim 9, wherein updating the wavelet transform products in the data library comprises categorizing the current wavelet transform product obtained from a patient based on one or both of the patient'"'"'s age and the patient'"'"'s gender so that the updated wavelet transform products may be searched using a future wavelet transform product derived from a future physiological signal.

11. A system for generating a data library, comprising:

a processor configured to;

acquire a plurality of physiological signals associated with a physiological condition of a patient;

perform a continuous wavelet transform on each physiological signal to generate a respective wavelet transform product for each physiological signal; and

organize the wavelet transform products so that the wavelet transform products may be searched using a current wavelet transform product derived from a current physiological signal; and

a memory configured to store the wavelet transform products.

12. The system, as set forth in claim 11, wherein the processor is configured to organize the wavelet transform products based on one or more of patient physiological data, recommendations, diagnoses, and patient characteristics.

13. The system, as set forth in claim 11, comprising a pulse oximetry sensor configured to acquire the plurality of physiological signals.

14. The system, as set forth in claim 13, wherein the processor is configured to organize the wavelet transform products based on characteristics of the pulse oximeter system.

15. The system, as set forth in claim 13, wherein the pulse oximeter sensor comprises an encoder configured to transmit information indicative of a type of the pulse oximeter sensor to the processor, and the processor is configured to organize the wavelet transform products based on the type of the pulse oximeter sensor.

16. The system, as set forth in claim 11, wherein the processor is configured to utilize a supervised learning technique to update the wavelet transform products in the data library with the current wavelet transform product.

17. A method, comprising:

receiving a plurality of physiological signals associated with a plurality of physiological conditions from a medical monitoring system at a processor;

performing, via the processor, a continuous wavelet transform on each physiological signal to generate a respective wavelet transform product for each physiological signal; and

categorizing, via the processor, the wavelet transform products in a data library based on characteristics of the medical monitoring system.

18. The method of claim 17, wherein the wavelet transform products in the data library are categorized based on a type of the medical monitoring system.

19. The method of claim 17, comprising:

obtaining a current physiological signal using a current medical monitoring system;

deriving a current wavelet transform product from the current physiological signal; and

identifying the wavelet transform products obtained via the medical monitoring system having similar characteristics to the current medical monitoring system, and comparing the identified wavelet transform products to the current wavelet transform product to identify the identified wavelet transform products having similar characteristics to the current wavelet transform product.

20. The method of claim 17, wherein the medical monitoring system is a pulse oximetry system.