Method and system for assaying agitation

• US 20060058590A1
• Filed: 08/24/2004
• Published: 03/16/2006
• Est. Priority Date: 08/24/2004
• Status: Abandoned Application

• Current Assignee: Canterprise Limited (University of Canterbury)
• Original Assignee: Canterprise Limited (University of Canterbury)
• Inventors: Chase, James Geoffrey, Shaw, Geoffrey M.
• Application Number:
  US10/925,395
• Publication Number:
  US 20060058590A1
• Time in Patent Office: Days
• US Class Current: 600/301
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/1106 to assess neuromuscular blockade, e.g. to estimate depth of anaesthesia
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/4035 Evaluating the autonomic nervous system
• CPC Class Codes: A61B 5/412 Detecting or monitoring sepsis
• CPC Class Codes: A61B 5/4821 Determining level or depth of anaesthesia A61B5/1106 takes precedence
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A method of physiologically quantifying patient agitation presented is based on reliable, objective physiological signals. The present invention is capable of quantifying autonomic nervous system interactions to provide an objective measurement of agitation. Adaptive autoregressive (AR) signal processing techniques are used to analyze heart rate (HRV) and blood pressure (BPV) variability and are combined with a fuzzy quantifier to measure agitation levels. Results show that agitation in normal subjects can be assessed and quantified using this approach, including differentiating periods of calm. Additionally, it has been shown that detected periods of agitation in ICU patients correlate well with subjective assessment by trained medical staff using the modified Riker SAS and with the objective assaying of patient motion. These results show that agitation can be quantitatively measured and assessed using common biomedical signals. Finally, agitation induced in normal subjects correlates well to agitation in ICU patients, as both show similar changes in the measured biomedical signals during agitated periods.

Claims

1. A method of objectively assaying agitation in an individual subject or patient, said method including;

automated monitoring of at least one metric of;

a. a patient'"'"'s autonomic nervous system (ANS);

b. expert systems or rules delineating other clinical events from agitation and/or

c. physical movement of one or more defined region(s) of interest (ROI) of the patient'"'"'s body,

performing signal processing on physiological signals associated with the monitored metric and

quantifying agitation from changes in said processed physiological signals.

2. The method as claimed in claim 1, wherein said quantifying agitation step provides a corresponding agitation value within a defined agitation index.

3. The method as claimed in claim 1, wherein said physiological signals include;

heart rate variability (HRV);

blood pressure (BP);

blood pressure variability (BPV);

respiratory rate (RR);

heart rate derivative (HRD);

blood pressure derivative (BPD);

temperature;

cardiovascular metrics, including cardiac output (CO), diastolic blood pressure, cardiac filling volumes;

EEG/brain wave measurements;

physical movement of one or more defined regions of interest (ROI) of the individual'"'"'s body.

4. The method as claimed in claim 1, wherein an automated monitoring of at least one metric associated with physical movement of one or more defined regions of interest (ROI) of the subject'"'"'s body, and comprises the steps:

image capture of at least one ROI;

determination of motion in a ROI;

quantification of relative subject agitation.

5. The method as claimed in claim 4, wherein said determination of motion in ROI step further includes the:

determination of power spectral density (PSD).

6. The method as claimed in claim 4, wherein said method includes the further step of

calculating a corresponding agitation value within a defined agitation index using a fuzzy logic inference system.

7. The method as claimed in claim 1, wherein the subject'"'"'s body is subdivided into defined regions of interest (ROI) according to the primary body portions likely to exhibit movement.

8. The method as claimed in claim 7, wherein the subject'"'"'s ROI include the subject'"'"'s limbs and head for a supine bedded subject.

9. The method as claimed in claim 4, wherein said determination of motion distinguishes between a subject'"'"'s motions and third party individuals.

10. The method as claimed in claim 9, wherein said third parties may include nursing of medical staff, patient relatives.

11. The method as claimed in claim 1, wherein, said at least one third party ROI is provided about the periphery of the captured image.

12. The method as claimed in claim 9, wherein movement detected in a third part ROI and subsequently detected in an adjacent subject'"'"'s ROI, causes the motion reading from the subject'"'"'s ROI to be de-weighted until the movement ceases.

13. The method as claimed in claim 1, wherein the automated monitoring apparatus includes an image detector.

14. The method as claimed in claim 11, wherein a normalized measure of motion power for both the subject'"'"'s ROI regions and third party ROI regions.

15. The method as claimed in claim 4, wherein said motion determination is performed using block comparison algorithm.

16. The method as claimed in claim 15, wherein said block comparison algorithm provides a single scalar index P(t), given by:

$P<br><br>\left(t\right)=\underset{x=1}{\overset{m}{\sum <br><br>}}<br><br>\underset{y=1}{\overset{n}{\sum <br><br>}}<br><br>{D_t<br><br>\left(x,y\right)}^2$

calculated from the sum power difference over successive captured image frames.

17. The method as claimed in claim 16, wherein P(t) is normalized with respect to the maximum attainable P(t) value.

18. The method as claimed in claim 4, wherein said determination of motion between captured image frames, or between ROI images is performed utilizing normalized correlation coefficients.

19. The method as claimed in claim 18, wherein said correlation coefficient r_K for a given region k is given by

$r_k$

( t + 1 ) = ∑

xy ⁢

{ ( f t ⁡

( x , y ) = f _ t ) ⁢

( f t + 1 ⁢

( x , y ) - f _ t + 1 ) } { ∑

xy ⁢

( f t ⁡

( x , y ) - f _ t ) 2 } ×

{ ∑

xy ⁢

( f t + 1 ⁢

( x , y ) - f _ t + 1 ) 2 } .

20. The method as claimed in claim 19, wherein a coefficient of determination, R_k=r²_k.

21. The method as claimed in claim 19, wherein a motion-related agitation index is defined as A_k(t+1)=−

r_k(t+1)²=1−

R_k(t+1).

22. The method as claimed in claim 21, wherein a single motion-related agitation index is calculated from the captured image frame-to-frame correlation coefficients r_K for both subject and third-party ROI motions using fuzzy mathematics.

23. The method as claimed in claim 2, wherein for a patient subject under nursing medical supervision, a patient agitation value on said agitation index is given by at least one of the following rules, wherein;

Rule Patient-motion Nurse-motion agitation 1. low low low 2. medium low medium 3. high low high 4. low medium low 5. medium medium high 6. high medium high 7. low high medium 8. medium high high 9. high high high

24. The method as claimed in claim 1, wherein said automated monitoring of at least one metric of a subject'"'"'s autonomic nervous system (ANS) includes monitoring power spectral density (PSD) of both HRV and BPV.

25. The method as claimed in claim 1, wherein said step of quantifying agitation include;

(QRS peak detection and R-R interval calculation) and/or (systolic and diastolic blood pressure values detection)

spectral estimation and calculation of PSD in VLF, LF, and HF frequency bands and

determination of subject agitation from changes in signal dynamics.

26. The method as claimed in claim 25, wherein said ORS peak detection and R-R interval calculation detected using a Haar wavelet.

27. The method as claimed in claim 25, wherein said spectral estimation and calculation of power in VLF, LF, and HF frequency bands is performed using frequency domain analysis.

28. The method as claimed in claim 27, wherein said frequency bands are defined as high (HF) 0.15-0.4 Hz;

low (LF) 0.07-0.14 Hz;

very low (VLF) 0.0033-0.04 Hz.

29. The method as claimed in claim 25, wherein said spectral analysis of R-R and/or systolic blood pressure signals is performed using an adaptive autoregressive (AR) spectral estimation method.

30. The method as claimed in claim 25, wherein said PSD, P_AR, is given by:

$P_{\mathrm{AR}}=T<br><br>\text{\hspace{1em}<br><br>}<br><br>{\mathrm{\sigma <br><br>}}_{\mathrm{\infty <br><br>}}<br><br>{<br><br>\frac{1}{A<br><br>\left(f\right)}<br><br>}^2.$

31. The method as claimed in claim 25, wherein said determination of subject agitation from changes in signal dynamics is determined using a fuzzy-logic inference system (FIS).

32. The method as claimed in claim 31, wherein inputs of said FIS include the HRV ratio VLF/MF and the BPV ratio HF/VLF.

33. The method as claimed in claim 31, wherein individual agitations levels for each input signal are recorded at a plurality of time increments T2, T3, T4, . . . Tn preceding an instantaneous level T1, wherein the individual agitation levels, obtained for HRV, systolic blood pressure and BPV, are then combined in create a single agitation value according to the rules:

Rule T1 T2 T3 T4 Agitation 1 Low —

—

—

Low 2 Medium High —

—

Low 3 Medium Medium Low Low Low 4 Medium Medium Medium Medium Low 5 Low Low Low Low Low 6 High High High High High 7 High Low Low Low High 8 High Medium Low Low High 9 High Medium Medium Medium Medium

36. A method of sedation administration including the steps;

objectively quantifying agitation according to the method claimed in claim 1;

inputting said quantified agitation to an automated sedation administration system,

administering defined quantities of one or more sedatives in proportion to said quantified agitation.

38. A fatigue and/or agitation monitoring method objectively quantifying agitation according to the method claimed in claim 1, characterised in that when a user'"'"'s physical movement from one or more ROI exceeds one or more upper or lower movement threshold levels, a signal is output to one or more systems including:

an audible and/or visual alarm signal,

a graphical and/or alphanumeric information display,

one or more direction and/or velocity control means of a vehicle,

audio system,

data-logging means.

39. A method of alerting nursing/medical personnel to excessive patient agitation, including the steps;

monitoring agitation in according to the method claimed in claim 1;

outputting an alarm signal when said quantified agitation exceeds one or more predetermined threshold values.

41. A means of quantifying user agitation in non-medical assessment environments including the steps;

monitoring agitation in according to the method claimed in claim 1,

wherein said quantified agitation is compared to established data recorded for non-stressed individuals to provide a relative agitation index.

34. A system for objective assaying of agitation in an individual or subject, said system including;

automated monitoring apparatus capable of monitoring at least one metric of

a. a patient'"'"'s autonomic nervous system (ANS);

b. expert systems or rules delineating other clinical events from agitation and/or

c. physical movement of one or more defined region(s) of interest (ROI) of the patient'"'"'s body,

signal processing means capable of processing physiological signals associated with the monitored metric and an subject'"'"'s autonomic nervous system (ANS) and/or

physical movement of one or more defined regions of interest (ROI) of the subject'"'"'s body,

signal processing means capable of processing physiological signals associated with the monitored metric and

processing means capable of calculating agitation from changes in said processing physiological signals.

37. A system for sedation administration including;

said system for objectively quantifying agitation as claimed in claim 34;

an automated sedation administration system capable of receiving said quantified agitation and administering defined quantities of one or more sedatives in proportion to said quantified agitation.

40. A system for alerting nursing/medical personnel to excessive patient agitation, including;

said system for objectively quantifying agitation as claimed in claim 34;

an alarm capable of outputting an alarm signal when said quantified agitation exceeds one or more predetermined threshold values.

35. The system as claimed in claim 35, wherein said agitation calculation provides a corresponding agitation value within a defined agitation index.