# US 8,543,196 B2 · Lie detection based on heart rate variability

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- Number: US 8,543,196 B2
- Document ID: US8543196B2
- Status: Granted
- Kind: B2
- Application number: 13/407,456
- Filed: 2012-02-28
- Published: 2012-06-21
- Granted: 2013-09-24
- Earliest claimed: 2009-05-15
- Assignees: Nanjing University
- Applicants: Xinbao Ning
- Inventors: Xinbao Ning
- Web page: https://patentgrep.com/patent/US8543196B2

## CPC

- A61B 5/024: Measuring pulse rate or heart rate
- A61B 5/02405: {Determining heart rate variability}
- A61B 5/164: {Lie detection}
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- Y10S 128/925: Neural network
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- Y10S: TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS

## Abstract

The present disclosure provides computer readable storage media useful in lie detection based on heart rate variability (HRV) analysis and HRV analysis using strange entropy. The computer readable storage medium may have a computer program encoded thereon, the computer program, when executed by a computer, instructs the computer to execute a method of lie detection that includes receiving an input associated with HRV and performing a nonlinear HRV analysis based on the input associated with HRV to obtain a lie detection result, the nonlinear HRV analysis.

## Claims

### Claim 1

A non-transitory computer-readable storage medium having a computer program encoded thereon, the computer program when executed by a computer instructs the computer to execute a method of lie detection, comprising:
receiving an input associated with heart rate variability; and
performing a heart rate variability analysis based on the input associated with heart rate variability to obtain a lie detection result, wherein the heart rate variability analysis includes at least one of nonlinear heart rate variability analysis and neural network-based linear heart rate variability analysis, and wherein the nonlinear heart rate variability analysis is performed by using at least one of strange entropy, Chaos, correlation dimension, fractal theory, strange attractors, mode entropy, multifractal, multiscale multifractal, Lyapunov index, base-scale entropy, and approximate entropy.

### Claim 2 (depends on 1)

The non-transitory computer-readable storage medium of claim 1, wherein the input associated with heart rate variability comprises a time series of electrocardiograph recordings.

### Claim 3 (depends on 1)

The non-transitory computer-readable storage medium of claim 1, wherein the heart rate variability analysis includes nonlinear heart rate variability analysis.

### Claim 4 (depends on 1)

The non-transitory computer-readable storage medium of claim 1, wherein the nonlinear heart rate variability analysis is performed using strange entropy.

### Claim 5 (depends on 1)

The non-transitory computer-readable storage medium of claim 1, wherein the nonlinear heart rate variability analysis comprises:
taking a time series with N elements, u:{u(i):1≦i≦N},wherein u(i) represents signals carried in the input associated with heart rate variability, and for each u(i) , there is a corresponding vector with m elements:
X(i)=[u(i), u(i+1), . . . , i−1)];
calculating a base scale BS(i) for each vector X(i);
transforming every X(i) to an m-dimensional symbol series Si={s(i), . . . −1)}, s∈A(A=0,1,2,3) based on a scale of a×BS(i);
calculating the probability of Si, wherein the probability of each different combination among the entire N−m+1m-dimensional vectors is:
p
⁡
(
π
)
=
#
⁢
{
t
⁢

(
u
i
,
…
⁢
,
u
t
+
m
-
1
)
⁢
⁢
has
⁢
⁢
type
⁢
⁢
π
}
N
-
m
+
1
,
wherein 1≦t≦N−m+1; # is the number of states, and each possible combination π for Si represents a vibration mode for Si;
obtaining nonlinear heart rate variability parameters or m-word distribution graph according to the probability of Si; and
obtaining a nonlinear heart rate variability parameter PSS, which is defined as sum probabilities of all strange states calculated according to the following equation:
PSS
=
∑
P
s
∑
i
=
0
255
⁢
P
i
;
wherein Pi is probability of combination i, Ps is probability of strange state, andΣPs is probability sum of all strange states.

### Claim 6 (depends on 1)

The non-transitory computer-readable storage medium of claim 1, wherein the lie detection result is obtained by real-time monitoring using a sliding window in a range from about 300 data points to about 500 data points, each of the data points representing a heartbeat.

### Claim 7 (depends on 1)

The non-transitory computer-readable storage medium of claim 1, wherein the neural network-based linear heart rate variability analysis comprises at least one 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.

### Claim 8

A non-transitory computer-readable storage medium having a computer program encoded thereon, the computer program when executed by a computer instructs the computer to execute a method of lie detection, comprising:
receiving an input associated with heart rate variability; and
performing a heart rate variability analysis based on the input associated with heart rate variability to obtain a lie detection result, wherein the heart rate variability analysis is performed using strange entropy.

### Claim 9 (depends on 8)

The non-transitory computer-readable storage medium of claim 8, wherein the input associated with heart rate variability includes a time series of electrocardiograph recordings.

### Claim 10 (depends on 8)

The non-transitory computer-readable storage medium of claim 8, wherein the nonlinear heart rate variability 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 heart rate variability, and for each u(i) , there is a corresponding vector with m elements:
X(i)=[u(i), u(i+1), . . . i−1)];
calculating a base scale BS(i) for each vector X(i);
transforming every X(i) to an m-dimensional symbol series Si={s(i),. . . −1)},s∈A(A =0,1,2,3)based on a scale of a×BS(i);
calculating probability of Si such that the probability of each different combination among the entire N−m+1m-dimensional vectors is:
p
⁡
(
π
)
=
#
⁢
{
t
⁢

(
u
i
,
…
⁢
,
u
t
+
m
-
1
)
⁢
⁢
has
⁢
⁢
type
⁢
⁢
π
}
N
-
m
+
1
,
wherein 1≦t≦N−m+1,# is the number of states, and each possible combination π for Si represents a vibration mode for Si; and
obtaining one or more of nonlinear heart rate variability parameters or m-word distribution graph according to the probability of Si.

### Claim 11 (depends on 10)

The non-transitory computer-readable storage medium of claim 10, wherein u(i) represents R-R interval.

### Claim 12 (depends on 10)

The non-transitory computer-readable storage medium of claim 10, wherein BS(i) is defined as square root average of difference between adjacent elements:
B
⁢
⁢
S
⁡
(
i
)
=
∑
j
=
1
m
-
1
⁢
(
u
⁡
(
i
+
j
)
-
u
⁡
(
i
+
j
-
1
)
)
2
m
-
1
.

### Claim 13 (depends on 10)

The non-transitory computer-readable storage medium of claim 10, wherein the every X(i) is transformed to an m-dimensional symbol series Si according to the transformation equation:
S
i
+
k
=
{
0
⁢
:
u
_
i
<
u
i
+
k
≤
u
_
i
+
a
×
B
⁢
⁢
S
⁡
(
i
)
1
⁢
:
u
i
+
k
>
u
_
i
+
a
×
B
⁢
⁢
S
⁡
(
i
)
2
⁢
:
u
_
i
-
a
×
B
⁢
⁢
S
⁡
(
i
)
<
u
i
+
k
≤
u
_
i
3
⁢
:
u
i
+
k
≤
u
_
i
-
a
×
B
⁢
⁢
S
⁡
(
i
)
;
wherein i=1, 2, . . . , ;Si+k is the k-th element of Si; ui+k is the k-th element of X(i); ūi represents the average value of the m-dimensional vector X(i);and 0,1,2, 3 are notations for region partition.

### Claim 14 (depends on 10)

The non-transitory computer-readable storage medium of claim 10, wherein the nonlinear heart rate variability parameters include H(m) which is calculated according to the following equation:
H(m)=−ΣP(π)log2 P(π).

### Claim 15 (depends on 10)

The non-transitory computer-readable storage medium of claim 10, wherein the nonlinear heart rate variability parameters include PSS which is defined as sum of probabilities of all strange states calculated according to following equation:
PSS
=
∑
P
s
∑
i
=
0
255
⁢
P
i
;
wherein Pi is probability of combination i, Ps probability of strange state, and ΣPs is probability sum of all strange states.

### Claim 16

A non-transitory computer-readable storage medium having a computer program encoded thereon, the computer program when executed by a computer instructs the computer to execute a method of lie detection, comprising:
receiving an input associated with heart rate variability; and
performing a nonlinear heart rate variability analysis based on the input associated with heart rate variability to obtain a lie detection result, the nonlinear heart rate variability analysis comprising:
obtaining a nonlinear heart rate variability parameter PSS, which is defined as sum probabilities of all strange states calculated according to the following equation:
PSS
=
∑
P
s
∑
i
=
0
255
⁢
P
i
;
wherein Pi is probability of combination i, Ps is probability of strange state, and ΣPs is probability sum of all strange states.

### Claim 17 (depends on 16)

The non-transitory computer-readable storage medium of claim 16, wherein the nonlinear heart rate variability 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 heart rate variability, and for each u(i) there is a corresponding vector with m elements:
X(i)=[u(i), u(i+1), . . . , i−1)];
calculating a base scale BS(i) for each vector X(i);
transforming every X(i) to an m-dimensional symbol series Si={s(i), . . . −1)},sεA(A =0,1,2,3) based on a scale of a×BS(i);
calculating probability of Si such that the probability of each different combination among the entire N−m+1 m-dimensional vectors is:
p
⁡
(
π
)
=
#
⁢
{
t
⁢

(
u
i
,
…
⁢
,
u
t
+
m
-
1
)
⁢
⁢
has
⁢
⁢
type
⁢
⁢
π
}
N
-
m
+
1
,
wherein 1≦t≦N−m+1,# is the number of states, and each possible combination π for Si represents a vibration mode for Si; and
obtaining one or more of nonlinear heart rate variability parameters or m-word distribution graphs according to the probability of Si.