Lie detection method and system

• US 8,155,733 B2
• Filed: 05/15/2009
• Issued: 04/10/2012
• Est. Priority Date: 05/15/2009
• Status: Expired due to Fees

• Current Assignee: Nanjing University
• Original Assignee: Nanjing University
• Inventors: Ning, Xinbao
• Primary Examiner(s): Layno, Carl H
• Assistant Examiner(s): WU, EUGENE T
• Application Number:
  US12/466,759
• Publication Number:
  US 20100292594A1
• Time in Patent Office: 1,061 Days
• Field of Search: 600/483, 600/508, 600/509, 128/925
• US Class Current: 600/509
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/164 Lie detection
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: Y10S 128/925 Neural network

Abstract

The present disclosure provides a heart rate variability (HRV) analysis-based method of lie detection, related computer program product, computer readable storage medium and system. Also provided is a method of HRV analysis using strange entropy.

Claims

1. A method of lie detection, comprising:

receiving an input associated with heart rate variability “

HRV”

; and

performing an HRV analysis based on the input associated with HRV to obtain a lie detection result;

wherein said HRV analysis includes nonlinear HRV analysis, wherein the nonlinear HRV analysis includes the following;

taking a time series with N elements, u ;

{u(i);

1 ≦

i≦

N}, wherein u(i) represents signals carried in the input associated with HRV, and for each u(i), there is a corresponding vector with m elements;

X(i)=[u(i),u(i+1), . . . , u(i+m−

1)];

calculating a base scale BS(i) for each vector X(i);

transforming every X(i) to a m-dimensional symbol series S_i={s(i), . . . s(i+m−

1)},sε

A(A=0, 1, 2,

     3) based on a scale of a×

BS(i);

calculating the probability of S_i, wherein the probability of each different combination among the entire N−

m+1 m-dimensional vectors is;

2. A lie detection system, comprising:

a computing unit configured to

receive an input associated with HRV; and

perform an HRV analysis based on the input associated with HRV to obtain a lie detection result;

wherein said HRV analysis includes nonlinear HRV analysis, wherein the nonlinear HRV analysis includes the following;

obtaining a nonlinear HRV parameter PSS, which is defined as sum probabilities of all strange states calculated according to the following equation;

3. The lie detection system according to claim 2, wherein said nonlinear HRV analysis is performed by using strange entropy method comprising:

taking a time series signal with N elements, u ;

{u(i);

1 ≦

i≦

N} ,wherein u(i) represents signals carried in the input associated with HRV, and for each u(i), there is a corresponding vector with m elements;

X(i)=[u(i),u(i+1), . . . , u(i+m−

1)];

calculating a base scale BS(i) for each vector X(i);

transforming every X(i) to a m-dimensional symbol series S_i={s(i), . . . s(i+m−

1)},sε

A(A=0, 1, 2,

     3) based on a scale of a×

BS(i);

calculating probability of S_i such that the probability of each different combination among the entire N−

m +1 m-dimensional vectors is;

4. A method of lie detection, comprising:

receiving an input associated with heart rate variability “

HRV”

; and

performing an HRV analysis based on the input associated with HRV to obtain a lie detection result;

wherein said HRV analysis includes one or more of nonlinear HRV analysis, or neural network-based linear HRV analysis, wherein said nonlinear HRV analysis is performed by using one or more methods selected from the group consisting of strange entropy (StEn), Chaos, correlation dimension, fractal theory, strange attractors, mode entropy (modEn), multifractal, multiscale multifractal, Lyapunov index, base-scale entropy, and approximate entropy (ApEn).

5. The method of lie detection according to claim 4, wherein said input associated with HRV includes a time series of ECG recordings.

6. The method of lie detection according to claim 4, wherein said HRV analysis is nonlinear HRV analysis.

7. The method of lie detection according to claim 6, wherein said nonlinear HRV analysis is performed by using one or more methods selected from the group consisting of strange entropy (StEn) and base-scale entropy.

8. The method of lie detection according to claim 7, wherein said nonlinear HRV analysis is performed by:

taking a time series signal with N elements, u;

{u(i);

1≦

i≦

N}, wherein u(i) represents signals carried in the input associated with HRV, and for each u(i), there is a corresponding vector with m elements;

X(i)=[u(i),u(i+1), . . . , u(i+m−

1)];

calculating a base scale BS(i) for each vector X(i);

transforming every X(i) to a m-dimensional symbol series S_i={s(i), . . . s(i+m−

1)},sε

A(A=0, 1, 2,

     3) based on a scale of a×

BS(i);

calculating probability of S_i such that the probability of each different combination among the entire N−

m+1m-dimensional vectors is;

9. The method of lie detection according to claim 8, wherein u(i) represents R-R interval.

10. The method of lie detection according to claim 8, wherein BS(i) is defined as square root average of difference between adjacent elements:

11. The method of lie detection according to claim 8, wherein said every X(i) is transformed to a m-dimensional symbol series S_i according to the transformation equation:

12. The method of lie detection according to claim 8, wherein said nonlinear HRV parameters include H(m) which is calculated according to the following equation:

H(m)=−

Σ

P(π

)log₂P(π

).

13. The method of lie detection according to claim 8, wherein said nonlinear HRV parameters include PSS which is defined as sum of probabilities of all strange states calculated according to following equation:

14. The method of lie detection according to claim 8, wherein m is 4, a is 0.2 and N is larger than 4^m.

15. The method of lie detection according to claim 8, wherein the lie detection result is obtained by real-time monitoring using a sliding window.

16. The method of lie detection according to claim 8, wherein the lie detection result is obtained by comparing the analysis results with normal data.

17. The method of lie detection according to claim 4, wherein said HRV analysis includes neural network-based linear HRV analysis comprising:

acquiring one or more linear HRV parameters from the input associated with HRV; and

performing a neural network analysis based on the linear HRV parameters.

18. The method of lie detection according to claim 17, wherein the neural network analysis is performed based on at least three linear HRV parameters selected from the group consisting of Total Power, Low Frequency Power, High Frequency Power, LFnorm, HFnorm, Low Frequency Power/High Frequency Power, Heart Rate, and Standard Deviation of adjacent R-R Interval.

19. The method of lie detection according to claim 4, wherein said HRV analysis includes neural network-based linear HRV analysis in combination with nonlinear HRV analysis, and wherein the neural network analysis is performed based on a combination of linear HRV parameters and nonlinear HRV parameters.

20. The method of lie detection according to claim 19, wherein said combination of linear HRV parameters and nonlinear HRV parameters includes two nonlinear HRV parameters H(m) and PSS and one to six linear HRV parameters selected from the group consisting of Total Power, Low Frequency Power, High Frequency Power, LFnorm, HFnorm, Low Frequency Power/High Frequency Power, Heart Rate, and Standard Deviation of adjacent R-R Interval.

21. The method of lie detection according to claim 4, further comprising outputting one or more of the analysis results or lie detection results.