Processing and detecting baseline changes in signals

• US 8,660,799 B2
• Filed: 10/03/2008
• Issued: 02/25/2014
• Est. Priority Date: 06/30/2008
• Status: Expired due to Fees

• Current Assignee: Nellcor Puritan Bennett Ireland
• Original Assignee: Nellcor Puritan Bennett Ireland
• Inventors: Watson, James, Addison, Paul Stanley
• Primary Examiner(s): NEGIN, RUSSELL SCOTT
• Application Number:
  US12/245,400
• Publication Number:
  US 20090326353A1
• Time in Patent Office: 1,971 Days
• Field of Search: None
• US Class Current: 702/19
• 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 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/1495 Calibrating or testing of in-vivo probes
• CPC Class Codes: A61B 5/6814 Head
• CPC Class Codes: A61B 5/6826 Finger
• CPC Class Codes: A61B 5/6838 Clamps or clips
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• 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
• 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 45/00 ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
• CPC Class Codes: G16B 5/00 ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
• CPC Class Codes: G16H 40/40 for the management of medical equipment or devices, e.g. scheduling maintenance or upgrades
• CPC Class Codes: G16H 40/63 for local operation

Abstract

According to embodiments, systems and methods for detecting the occurrence of events from a signal are provided. A signal processing system may analyze baseline changes and changes in signal characteristics to detect events from a signal. The system may also detect events by analyzing energy parameters and artifacts in a scalogram of the signal. Further, the system may detect events by analyzing both the signal and its corresponding scalogram.

Claims

1. A method of detecting the occurrence of events from a signal, comprising:

receiving the signal;

transforming, using a processor, the signal to generate a transformed signal;

generating, using the processor, a scalogram based at least in part on the transformed signal;

detecting an artifact within the scalogram, wherein the artifact is a high-energy broad-scale cone shaped region of the scalogram;

calculating characteristics of the detected artifact; and

analyzing, using the processor, the characteristics of the detected artifact within the scalogram to detect an occurrence of a physiological event.

2. The method of claim 1, further comprising:

calculating a first energy parameter within a first region of the scalogram;

calculating a second energy parameter within a second region of the scalogram subsequent to the artifact; and

further analyzing the first and second energy parameters to detect the occurrence of the event.

3. The method of claim 2, wherein the first energy parameter is the average energy within the first region and the second energy parameter is the average energy within the second region.

4. The method of claim 2, wherein the signal is a photoplethysmograph and wherein the first and second regions comprise at least a portion of a pulse band.

5. The method of claim 4, wherein the first and second regions comprise a ridge of the pulse band.

6. The method of claim 1, wherein the event comprises at least one of changes in blood pressure, changes in body position, vasodilation, vasoconstriction, sympathetic nervous response, parasympathetic nervous response, and sleep arousal.

7. The method of claim 1, further comprising performing an action based at least in part on the detected event.

8. The method of claim 7, wherein performing the action comprises recalibrating a device based at least in part on the detected event or setting a flag of the detected event.

9. The method of claim 1, further comprising:

calculating at least one signal characteristic of the signal; and

further analyzing the at least one signal characteristic to detect the occurrence of the event.

10. The method of claim 1, further comprising:

detecting a baseline change in the signal;

calculating a first energy parameter within a first region of the scalogram;

calculating a second energy parameter within a subsequent second region of the scalogram subsequent; and

further analyzing the baseline change and the first and second energy parameters to detect the occurrence of the event.

11. A system for detecting the occurrence of events from a signal, comprising:

a signal generator capable of generating the signal; and

a processor capable of;

transforming the signal to generate a transformed signal;

generating a scalogram based at least in part on the transformed signal;

detecting an artifact within the scalogram, wherein the artifact is a high-energy broad-scale cone shaped region of the scalogram;

calculating characteristics of the detected artifact; and

analyzing, using the processor, the characteristics of the detected artifact within the scalogram to detect an occurrence of a physiological event.

12. The system of claim 11, wherein the processor is further capable of:

calculating a first energy parameter within a first region of the scalogram;

calculating a second energy parameter within a second region of the scalogram subsequent to the artifact; and

further analyzing the first and second energy parameters to detect the occurrence of the event.

13. The system of claim 12, wherein the first energy parameter is the average energy within the first region and the second energy parameter is the average energy within the second region.

14. The system of claim 12, wherein the signal is a photoplethysmograph and wherein the first and second regions comprise at least a portion of a pulse band.

15. The system of claim 14, wherein the first and second regions comprise a ridge of the pulse band.

16. The system of claim 12, wherein the artifact is a high energy broad-scale cone.

17. The system of claim 11, wherein the event comprises at least one of changes in blood pressure, changes in body position, vasodilation, vasoconstriction, sympathetic nervous response, parasympathetic nervous response, and sleep arousal.

18. The system of claim 11, wherein the processor is further configured to initiate an action based at least in part on the detected event.

19. The system of claim 18, wherein the action comprises recalibrating a device based at least in part on the detected event or setting a flag of the detected event.

20. The system of claim 11, wherein the processor is further capable of:

calculating at least one signal characteristic of the signal; and

further analyzing the at least one signal characteristic to detect the occurrence of the event.

21. The system of claim 11, wherein the processor is further capable of:

detecting a baseline change in the signal;

calculating a first energy parameter within a first region of the scalogram;

calculating a second energy parameter within a subsequent second region of the scalogram subsequent; and

further analyzing the baseline change and the first and second energy parameters to detect the occurrence of the event.

22. A non-transitory computer-readable medium for use in detecting the occurrence of events from a signal, the computer-readable medium having computer program instructions recorded thereon for:

receiving the signal;

transforming the signal to generate a transformed signal;

generating a scalogram based at least in part on the transformed signal;

detecting an artifact within the scalogram, wherein the artifact is a high-energy broad-scale cone shaped region of the scalogram;

calculating characteristics of the detected artifact; and

analyzing, using the processor, the characteristics of the detected artifact within the scalogram to detect an occurrence of a physiological event.