CN111096753A - Near infrared spectrum multi-channel detection method suitable for complex acceleration condition - Google Patents

Near infrared spectrum multi-channel detection method suitable for complex acceleration condition Download PDF

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CN111096753A
CN111096753A CN201910998308.2A CN201910998308A CN111096753A CN 111096753 A CN111096753 A CN 111096753A CN 201910998308 A CN201910998308 A CN 201910998308A CN 111096753 A CN111096753 A CN 111096753A
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李德玉
夏美云
顾晓磊
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Beihang University
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Abstract

The invention discloses a near infrared spectrum multi-channel detection method suitable for complex acceleration conditions, which uses an NIRS signal acquisition system and a six-channel Inertial Measurement Unit (IMU) to synchronously record data; establishing an external input autoregressive model (ARX), taking six-channel IMU data as external input of a system, and estimating motion artifacts in the detected NIRS signals; obtaining the effective physiological information in the NIRS signal under the condition of complex acceleration. The invention has the advantages that: better modeling of motion artifacts can be achieved, so that motion artifacts in the detected signal can be successfully removed, and the obtained NIRS signal without motion artifacts can more accurately detect physiological changes.

Description

Near infrared spectrum multi-channel detection method suitable for complex acceleration condition
Technical Field
The invention belongs to the technical field of medical treatment, and particularly relates to a near infrared spectrum multi-channel detection method suitable for complex acceleration conditions.
Background
Near infrared spectroscopy (NIRS) can nondestructively detect the concentration variation of oxygenated hemoglobin and deoxygenated hemoglobin in brain tissue, and is an effective means for measuring the local blood flow dynamics information and metabolism status of the brain. In addition, NIRS is more popular than other brain function measurement devices, such as positron emission tomography (PER) and Magnetic Resonance Imaging (MRI), due to its advantages of non-invasiveness, safety, simplicity of operation, and cost effectiveness, making it an excellent choice for continuously monitoring cerebral hemodynamic conditions.
In NIRS measurements where the light source and detector are coupled directly to the human scalp, movement of the subject's head or body is likely to cause the appearance of Motion Artifacts (MAs) that can cause the photoelectrode to decouple from the scalp, thereby causing high frequency spikes or baseline shifts in the signal, which can severely affect the quality of the measured signal. NIRS signal variations due to physiological changes and variations due to motion artifacts overlap each other closely, ultimately affecting the reliability of subsequent analysis processing results.
Chinese patent application No. CN103750845A, "a method for automatically removing motion artifacts of near infrared spectrum signal", discloses a method for automatically removing motion artifacts of near infrared spectrum signal, which can automatically select a threshold and detect an interval containing motion artifacts based on the probability distribution characteristics of the near infrared spectrum signal, perform empirical mode decomposition on the near infrared signal, and process an obviously abnormal eigenmode component to remove the motion artifacts. The invention only deals with the obviously abnormal mode. In actual application of NIRS, under a weak exercise condition that cannot be actively avoided by a test, such as a newborn or an animal, the whole data set may be excluded from a study, resulting in a great loss of data and a waste of experimental investment.
Disclosure of Invention
In order to solve the problems in the prior art, the invention aims to overcome the defects in the prior art, and provides a near infrared spectrum multi-channel detection method suitable for complex acceleration conditions, which can better model motion artifacts, so that the motion artifacts can be removed more accurately.
The invention is suitable for a near infrared spectrum multi-channel detection method under a complex acceleration condition, and comprises the following three steps:
step 1: and collecting frontal lobe near infrared data of the testee and synchronous IMU data of the near infrared optical detector at the same site in the test process.
Step 2: extracting 1, 2, 3, …, N segments of data, each segment having corresponding NIRS and IMU data segments, for each segment:
(1) and modeling by taking the IMU data segment as external input of the system.
(2) And establishing an autoregressive model to obtain the motion artifact in the NIRS data segment.
(3) And removing the motion artifact in the NIRS data segment.
And step 3: and converting the NIRS data after the artifact is removed into blood oxygen concentration change.
The invention has the advantages that:
1. the near infrared spectrum multi-channel detection method is suitable for the near infrared spectrum multi-channel detection method under the complex acceleration condition, and can realize better modeling on the motion artifact, so that the motion artifact in the detection signal can be successfully removed, and the obtained NIRS signal without the motion artifact can more accurately detect the physiological change.
2. The invention is suitable for near infrared spectrum multi-channel detection method under complex acceleration condition, and artifact removal from NIRS signal is realized on the original signal of the optical sensor, therefore, after removing the motion artifact, the NIRS signal can be used in any other processing for further research.
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FIG. 1 is an overall flow chart of a near infrared spectrum multi-channel detection method suitable for complex acceleration conditions according to the present invention;
FIG. 2 is a schematic diagram of the structure of the attachment of a near infrared optical detector and an IMU in the near infrared spectrum multi-channel detection method suitable for complex acceleration conditions of the present invention;
FIG. 3 is a schematic diagram of the synchronous IMU data acquisition process of frontal lobe near infrared data and near infrared optical detector co-location in the near infrared spectrum multi-channel detection method for complex acceleration conditions.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
The present invention will be described in further detail with reference to the accompanying drawings.
The invention is suitable for a near infrared spectrum multi-channel detection method under a complex acceleration condition, and comprises the following three steps:
step 1: and collecting frontal lobe near infrared data of the testee and synchronous IMU data of the near infrared optical detector at the same site in the test process.
The test process is as follows:
a. setting a computer screen to display as an empty screen;
b. and after the subject wears the NIRS equipment and the near-infrared optical detector, the mounting is finished, and the waiting time is 10 s.
c. A "+" sign is displayed at a random position on a computer screen, and a subject watches the sign at the same time; the "+" sign shows a time duration of 2 s;
d. after the interval of 10s, the process returns to step c.
The above process was repeated 20 times.
In the test process, the frontal lobe near-infrared data of the subject is collected by NIRS equipment in real time to form a head covering area, including all near-infrared signals of a frontal lobe brain area, and the signals can reflect hemodynamic activity; the near-infrared signals of at least 1 signal channel (1 emission light source and 1 corresponding acquisition probe) of the NIRS equipment are acquired, namely the near-infrared signals of only one channel can be acquired, the near-infrared signals of a plurality of channels can also be acquired, and different SD (secure digital) images can be arranged on the near-infrared signals according to different acquired channel numbers. Synchronous IMU data of the near-infrared optical detector at the same site is acquired in real time by a six-channel IMU chip which is attached to the back of a probe of the near-infrared optical detector and comprises a three-channel accelerometer and a three-channel gyroscope.
Step 2: extracting data segments, establishing an external input autoregressive model (ARX), taking six-channel IMU data as external input of a system, and estimating motion artifacts in the detected NIRS signals;
sequentially extracting data segments corresponding to the IMU data fluctuation segments, wherein the data segments are corresponding NIRS and IMU data segments, the data segments are marked as 1, 2, 3, … and N, and the following processes are respectively carried out on each data segment:
(1) modeling with IMU data segment u [ k ] as external input to the system:
x[n]＝s[n]+w[n](1)
wherein x [ n ] represents the hemodynamic signal detected by the near-infrared optical detector, the signal is obtained according to the near-infrared signal acquired by NIRS, s [ n ] represents the real hemodynamic signal, and w [ n ] represents the motion artifact signal.
(2) ARX modeling:
Figure BDA0002240459550000031
wherein the content of the first and second substances,
Figure BDA0002240459550000032
is based on the model coefficient a ═ a1a2…aNA]And b ═ b0b1…bNB]The input of the model is u ═ u [ k [ ]],u[k-1],u[k-2],……,u[k-NB]]；
Figure BDA0002240459550000033
Is a 1 xL coefficient vector, u [ k ]]Is the lx1 input vector and dimension L is the number of IMU data channels used. NA and NB are divided into a total number of coefficients a and b, u [ k ]]For IMU data segments, u [ k ]]＝[Ax[k]Ay[k]Az[k]Gx[k]Gy[k]Gz[k]]T，Ax，AyAnd AzRepresenting three accelerometer channel data, Gx，GyAnd GzRepresenting three gyroscope data channel data.
The coefficients a and b may be determined by a least squares method:
Figure BDA0002240459550000034
where k is the sampling point of the data segment, and each sampling point has a corresponding NIRS data and IMU data, corresponding to coefficients a, b, respectively.
(3) The motion artifact is removed from the NIRS data segment, and the estimated real hemodynamic signal can be expressed as:
Figure BDA0002240459550000035
finally, the motion artifacts in the N NIRS data segments are completely removed, and the obtained NIRS signals without the motion artifacts can more accurately detect physiological changes.
And step 3: the artifact removed NIRS data from step 3 is converted to a change in blood oxygen concentration (HbO) using the typical Beer-Lambert law2And Hb change).

Claims (4)

1. A near infrared spectrum multi-channel detection method suitable for complex acceleration conditions comprises the following three steps:
step 1: collecting frontal lobe near infrared data of a subject and synchronous IMU data of a near infrared optical detector at the same site in the test process;
step 2: extracting 1, 2, 3, …, N segments of data, each segment having corresponding NIRS and IMU data segments, for each segment:
(1) modeling by taking the IMU data segment as external input of the system;
(2) establishing an autoregressive model to obtain motion artifacts in an NIRS data segment;
(3) removing motion artifacts from the NIRS data segment;
and step 3: and converting the NIRS data after the artifact is removed into blood oxygen concentration change.
2. The near infrared spectrum multi-channel detection method suitable for the complex acceleration condition as claimed in claim 1, characterized in that: collecting frontal lobe near-infrared data of a subject in real time by an NIRS device, wherein the frontal lobe near-infrared data comprise all near-infrared signals of a frontal lobe brain region; near infrared signals of at least 1 signal channel of the NIRS device are collected.
3. The near infrared spectrum multi-channel detection method suitable for the complex acceleration condition as claimed in claim 1, characterized in that: synchronous IMU data of the near-infrared optical detector at the same site is acquired in real time by a six-channel IMU chip which is attached to the back of a probe of the near-infrared optical detector and comprises a three-channel accelerometer and a three-channel gyroscope.
And sequentially extracting data segments corresponding to the IMU data fluctuation segments, wherein the data segments are corresponding NIRS and IMU data segments, and the data segments are marked as 1, 2, 3, …, N.
4. The near infrared spectrum multi-channel detection method suitable for the complex acceleration condition as claimed in claim 1, characterized in that: ARX was modeled as follows:
Figure RE-FDA0002427553100000011
wherein the content of the first and second substances,
Figure RE-FDA0002427553100000012
is based on the model coefficient a ═ a1a2…aNA]And b ═ b0b1…bNB]The input of the model is u ═ u [ k [ ]],u[k-1],u[k-2],……,u[k-NB]]；
Figure RE-FDA0002427553100000013
Is a 1 xL coefficient vector, u [ k ]]Is the lx1 input vector and dimension L is the number of IMU data channels used; NA and NB are divided into a total number of coefficients a and b, u [ k ]]For IMU data segments, u [ k ]]＝[Ax[k]Ay[k]Az[k]Gx[k]Gy[k]Gz[k]]T，Ax，AyAnd AzRepresenting three accelerometer channel data, Gx，GyAnd GzRepresenting three gyroscope data channel data;
the coefficients a and b may be determined by a least squares method:
Figure RE-FDA0002427553100000014
where k is the sampling point of the data segment, and each sampling point has a corresponding NIRS data and IMU data, corresponding to coefficients a, b, respectively.
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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107157492A (en) * 2017-05-19 2017-09-15 国家电网公司 A kind of embedded human physiologic information non-invasive detection system and data processing method

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107157492A (en) * 2017-05-19 2017-09-15 国家电网公司 A kind of embedded human physiologic information non-invasive detection system and data processing method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
JEFFREYW. BARKER等: "Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS", 《BIOMEDICAL OPTICS EXPRESS》 *
MASUDUR R. SIDDIQUEE等: "Movement artifact removal from NIRS signal using multi‑channel IMU data", 《BIOMEDICAL ENGINEERING ONLINE》 *

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