CN105796091B - A kind of intelligent terminal for removing electrocardiosignal vehicle movement noise - Google Patents
A kind of intelligent terminal for removing electrocardiosignal vehicle movement noise Download PDFInfo
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- CN105796091B CN105796091B CN201610113597.XA CN201610113597A CN105796091B CN 105796091 B CN105796091 B CN 105796091B CN 201610113597 A CN201610113597 A CN 201610113597A CN 105796091 B CN105796091 B CN 105796091B
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
- A61B5/7207—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
- A61B5/721—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
Abstract
The invention discloses a kind of intelligent terminals for removing electrocardiosignal vehicle movement noise, including processor and bluetooth communication unit；Processor is loaded with signal acquisition module, preprocessing module, neural metwork training module and denoising module.Intelligent terminal of the present invention acquires exercise data as neural network input sample using 3-axis acceleration sensor, it is pre-processed by the dynamic electrocardiogram (ECG) data of format conversion and method for normalizing before neural metwork training when and movement static to human body and the deviation that makes between the two is used as the supervision of neural network, and then noise model is established with the optimal way of ratio conjugate gradient using artificial neural network learning algorithm, the dynamic electrocardiogram (ECG) data obtained with monitoring subtracts corresponding motion artifacts and completes denoising.Whole equipment is easy and is easily achieved, and vehicle movement noise can be effectively automatically removed during cardioelectric monitor, obtains accurate electrocardiogram (ECG) data, guarantee is provided to the diagnosis and treatment of angiocardiopathy for doctor.
Description
Technical field
The invention belongs to technical field of medical equipment, and in particular to a kind of for removing electrocardiosignal vehicle movement noise
Intelligent terminal.
Background technology
Holter is for continuously recording 24 hours dynamic electrocardiogram activity datas, including rest, activity, work, dining
Etc. electrocardio-activity data under different situations, can help for finding the symptoms such as arrhythmia cordis and myocardial ischemia, be clinical point
The analysis state of an illness and diagnosis and treatment provide important foundation.With emerging in large numbers for portable wearable cardiac monitoring equipment so that user can be each
Very easily cardioelectric monitor is carried out under kind of state.But Holter signal is highly susceptible to the interference of various noises, it is main
Interfere AC influence, myoelectricity interference, baseline drift, conducting wire connection error, electrode slice and skin contact insufficient etc., with
The development of technology, most of Holter equipment is all configured with various types of filters to eliminate these interference, but
Be portable dynamic ecg equipment on a moving vehicle in use, the interference of vehicle movement may seriously limit dynamic
The quality of ECG signal.
The motion artifacts of vehicle can act on object onboard, including the cardioelectric monitor equipment that human body is dressed.And vehicle
The state of movement can reflect influence of the interference to cardioelectric monitor equipment well, can be non-by 3-axis acceleration sensor
The motion state for often easily obtaining vehicle carries to analyze the influence mode and degree of movement to eliminate vehicle movement interference
For data basis.
3-axis acceleration sensor is a kind of sensor that physical signal acceleration is converted into the electric signal convenient for measuring,
The operation principle of current most of 3-axis acceleration sensors is pressure resistance type, piezoelectric type and condenser type, the acceleration direct ratio of generation
In the variation of resistance, voltage and capacitance, by calculating the relationship between these variable quantities and acceleration, you can calculate acceleration
Value.3-axis acceleration sensor can under the occasion for not knowing object moving state in advance, by detect x, tri- axis of y, z
Signal obtains its coordinate components, to the motion state of accurate judgement object, has small, light-weight characteristic point, can be with
Steric acceleration is measured, the kinetic property of object can be accurately reflected comprehensively, in aerospace, automobile, robot and medicine etc.
Field obtains extensive use.
Most portable holter devices currently on the market, simple structure do not eliminate vehicle movement noise work(
Can, it is easily used to be interfered under state of motion of vehicle and causes Holter jitter, the ecg wave form of record
It is not used to ecg analysis, not only limits the use scope of dynamic ecg monitoring equipment, it is also possible to make doctor to cardiovascular disease
The diagnosis and treatment of patient cause serious consequence.
Invention content
For the above-mentioned technical problem present in the prior art, the present invention provides one kind for removing electrocardiosignal vehicle
The intelligent terminal of motion artifacts can effectively automatically remove vehicle movement noise during cardioelectric monitor, obtain accurate
Electrocardiogram (ECG) data provides guarantee for doctor to the diagnosis and treatment of angiocardiopathy.
A kind of intelligent terminal for removing electrocardiosignal vehicle movement noise, including processor and bluetooth communication unit,
The processor is loaded with following functions module：
Signal acquisition module, for collecting the electrocardio that cardioelectric monitor device provides by the bluetooth communication module in intelligent terminal
Training data and 3-axis acceleration sensor under each Heart Rate States of correspondence that data, electrocardiosignal simulative generator provide carry
The exercise data of confession；The electrocardiogram (ECG) data is the daily threeway detected by cardioelectric monitor device in vehicle movement of user
Road ECG detecting signal segment；The training data include electrocardiosignal simulative generator generated when static be in any heart
The static electrocardiosignal section of m group triple channels under rate state and the m under any Heart Rate States generated in vehicle movement
Group triple channel exercise electrocardiogram signal section, m are the natural number more than 1；The exercise data includes that 3-axis acceleration sensor is same
Step acquisition corresponding three axis of the generated triple channel exercise electrocardiogram signal section in vehicle movement with electrocardiosignal simulative generator
Acceleration signal section and corresponding with the triple channel ECG detecting signal segment that cardioelectric monitor device is detected in vehicle movement
3-axis acceleration signal segment；
Preprocessing module, for the static electrocardiosignal section of triple channel, triple channel exercise electrocardiogram signal section, threeway
Road ECG detecting signal segment and 3-axis acceleration signal segment are pre-processed；Simultaneously for any Heart Rate States, make the heart rate
Pretreated triple channel exercise electrocardiogram signal section work corresponding with the static electrocardiosignal section of triple channel is poor under state, obtains m groups three
Channel electrocardio motion artifacts；
Neural metwork training module moves any Heart Rate States according to the triple channel electrocardio under the Heart Rate States
Noise and pretreated 3-axis acceleration signal segment are trained by artificial neural network learning algorithm, are obtained being directed to and are somebody's turn to do
Vehicle movement noise model of the Heart Rate States about three lead electrocardiosignals；
Denoising module, will three axis corresponding with triple channel ECG detecting signal segment for the Heart Rate States residing for user
Acceleration signal section inputs output in corresponding vehicle movement noise model and obtains a set of three links road electrocardio motion artifacts, and then makes
The triple channel ECG detecting signal segment subtracts the triple channel electrocardio motion artifacts and is eliminated after vehicle movement noise
Triple channel electrocardiogram (ECG) data.
The preprocessing module is to the static electrocardiosignal section of triple channel, triple channel exercise electrocardiogram signal section, the triple channel heart
It includes format conversion and normalized that power detection signal section and 3-axis acceleration signal segment, which carry out pretreatment, to obtain appropriate lattice
Data-signal in formula and range.
The preprocessing module is normalized based on following formula：
Wherein：X is the either signal value in signal segment, xminAnd xmaxMinimum value and maximum value respectively in signal segment,
X' is signal values of the corresponding x after normalized.
Artificial neural network learning algorithm used by the neural metwork training module is made with ratio conjugate gradient method
To optimize direction.
The detailed process that the neural metwork training module is trained by artificial neural network learning algorithm is as follows：
(1) m groups 3-axis acceleration signal segment corresponding with triple channel exercise electrocardiogram signal section after pretreatment is divided into training
Collection is with test set and training set is more than test set；
(2) one neural network being made of input layer, hidden layer and output layer of initialization structure；
(3) appoint from training set take one group of 3-axis acceleration signal segment substitute into above-mentioned neural computing obtain it is corresponding about
The output of electrocardio motion artifacts is transported as a result, calculating the output result with the triple channel electrocardio corresponding to the 3-axis acceleration signal segment
Accumulated error between moving noise；
(4) according to the accumulated error by gradient descent method in neural network between input layer and hidden layer and hide
Weight between layer and output layer is modified, and then is appointed from training set and removed one group of 3-axis acceleration signal segment substitution amendment
Neural network afterwards；
(5) according to all 3-axis acceleration signal segments in step (3) and (4) traversal training set, take accumulated error minimum
When corresponding neural network be vehicle movement noise model.
Input layer is made of 3 neurons in the neural network of the neural metwork training module initialization structure, hidden
It hides layer to be made of 10 neurons, output layer is made of 3 neurons.
The expression formula of neuron function g (z) is such as in the neural network of the described neural metwork training module initialization structure
Under：
Wherein：Z is argument of function.
The vehicle movement noise model that the neural metwork training module obtains training, by three axis in test set
Acceleration signal section substitutes into the vehicle movement noise model and obtains the corresponding output about electrocardio motion artifacts as a result, making survey one by one
Examination concentrates the output result corresponding to each group of 3-axis acceleration signal segment to be compared with triple channel electrocardio motion artifacts, if surveying
Examination be concentrated with a certain proportion of 3-axis acceleration signal segment comparison result be less than or equal to threshold value if, then the vehicle movement noise
Model finally determines；Otherwise acquiring more threeways using electrocardiosignal simulative generator and 3-axis acceleration sensor
The static electrocardiosignal section in road, triple channel exercise electrocardiogram signal section and 3-axis acceleration signal segment are through the signal acquisition in intelligent terminal
The input of scale as neural metwork training module after module and preprocessing module to increase training set is trained.
The intelligent terminal can be smart mobile phone, tablet computer or PC machine.
Intelligent terminal of the present invention is led to using 3-axis acceleration sensor acquisition exercise data as neural network input sample
Dynamic electrocardiogram (ECG) data when crossing static to human body before neural metwork training format conversion and method for normalizing and movement carries out pre-
It handles and makes deviation between the two as the supervision of neural network, and then is total with ratio using artificial neural network learning algorithm
The optimal way of yoke gradient establishes noise model, and the dynamic electrocardiogram (ECG) data obtained with monitoring subtracts corresponding motion artifacts and completes
Denoising.Whole equipment is easy and is easily achieved, and vehicle movement noise can be effectively automatically removed during cardioelectric monitor, is obtained
To accurate electrocardiogram (ECG) data, guarantee is provided to the diagnosis and treatment of angiocardiopathy for doctor.
Description of the drawings
Fig. 1 is the structural schematic diagram of intelligent terminal of the present invention.
Fig. 2 is the artificial nerve network model schematic diagram during electrocardiosignal denoising of the present invention.
Specific implementation mode
In order to more specifically describe the present invention, below in conjunction with the accompanying drawings and specific implementation mode is to technical scheme of the present invention
It is described in detail.
As shown in Figure 1, for removing the intelligent terminal of electrocardiosignal vehicle movement noise using intelligent hand in the present embodiment
Machine includes processor and bluetooth communication module in the smart mobile phone, and bluetooth communication module is connected with processor；Processor includes
Signal acquisition module, preprocessing module, neural metwork training module and denoising module；Wherein：
Bluetooth communication module is communicated for intelligent terminal with electrocardiogram monitoring terminal, and intelligent terminal sends commands to prison
Terminal is surveyed, monitoring terminal response command uploads electrocardiogram (ECG) data to intelligent terminal.
Signal acquisition module is used to receive the dynamic electrocardiogram (ECG) data from generator or monitor by bluetooth communication module
And carry out the motion state data of acceleration sensor；Dynamic electrocardiogram (ECG) data includes that m groups are acquired in stationary state, is in
Electrocardiosignal sequence E under a kind of Heart Rate States then has the static electrocardiogram (ECG) data E of n*m groups due to the Heart Rate States for having n kinds different；m
What group was acquired in state of motion of vehicle, the electrocardiosignal sequence M under a kind of Heart Rate States, due to the heart rate for having n kinds different
State then has n*m group exercise ECG data M.
Motion state data is what m groups were acquired in state of motion of vehicle, corresponds to three axis under wherein a kind of Heart Rate States
Acceleration sensor outputs signals sequence Q [x, y, z] then has n*m group motion state sequences due to the Heart Rate States for having n kinds different
Q [x, y, z] is arranged, m, n are the natural number more than 1.
Preprocessing module is used to carry out format conversion and normalized to dynamic electrocardiogram (ECG) data and motion state data, obtains
To appropriate format and the initial data of range.In the present embodiment, data sampling rate 250, AD conversion digit is 24bit, is passed through
Sample rate is reduced to 200 by down-sampled algorithm, and 24bit data are converted to 16bit by data compression algorithm, obtain capacity smaller
Data, but need to meet the needs of neural metwork training module.Normalization algorithm uses linear transformation algorithm, and expression formula is：
Wherein：X is input vector, and max is the maximum value of x, and min is the minimum value of x, and f (x) is the later output of normalization
Vector.
Neural metwork training module passes through artificial neuron to the pretreated motion state operational data sequence of above-mentioned n*m groups
Network depth learning algorithm is trained and tests, and obtains noise model；Specific implementation is as follows：
(1) the pretreated motion state data sequence Q [x, y, z] being under a kind of Heart Rate States of m groups is divided into training
Collection is with test set and training set is more than test set.
(2) initial neural network model is established according to artificial neural network learning algorithm：The neural network model is by inputting
Layer, hidden layer, output are connected between input layer and hidden layer by formula (1) at up of three layers, and the input of input layer is three axis
The activation primitive of acceleration transducer x, y, the operational data sequence of z-axis, hidden layer and output layer is formula (2), hidden layer by
10 neurons are constituted, and the output of output layer is 3-axis acceleration sensor x, y, the noise of z-axis, the neural network model of foundation
As shown in Figure 2.
Wherein,WithFor the coefficient of hidden layer, g is tansig functions, i=1,2 ..., 10, j=1,2,3.
Wherein,WithFor the coefficient of output layer, i=1,2,3, j=1,2 ..., 10.
(3) one group of sample data of training set is input to the neural network under current weight coefficient, calculates input successively
The output of each node of layer, hidden layer, output layer.
(4) it is calculated between the output layer output of all training samples and the expected result of training sample according to formula (3)
Accumulated error E corrects the weight coefficient between input layer and each node of hidden layer according to conjugate gradient decent according to formula (4),
The weight coefficient between hidden layer and each node of output layer is corrected according to formula (5)：
Wherein：E is accumulated error,K-th of output of the output layer for single training sample Jing Guo neural network, okFor
K-th of expected result of single training sample, m are training set total sample number, and p is output layer output sum.
Wherein：who(t) weight coefficient when being input to neural network for the t times sample between hidden layer and output layer,
The output of output layer for single training sample Jing Guo neural network, o are the expected result of single training sample, xhFor hidden layer
Output, α is learning rate.
Wherein：wih(t) weight coefficient when being input to neural network for the t times sample between input layer and hidden layer, xi
For the output of input layer.
(5) it repeats step (3) and step (4) traverses all training sets, establish noise model, obtain weights when E minimums
Then noise model corresponding to coefficient sets is tested the noise model using test set, if the accuracy of test is high
In threshold value, then the noise model is best model；If the accuracy of test is less than threshold value, training sample repetition is continued growing
Step (3) and step (4) train neural network, until training is completed.Trained obtained weight coefficient matrix is in the present embodiment：
Input layer and hidden layer：
Hidden layer and output layer：
Denoising module is used for the weights proportion according to each layer of neural network, restores the system function of noise model；By user
The 3-axis acceleration output data for the embodiment human motion state that daily monitoring obtains substitutes into noise model, and output is made an uproar accordingly
Sound as a result, then using the dynamic electrocardiogram (ECG) data that monitor obtains subtract noise model output noise figure, obtain removal noise
Dynamic electrocardiogram (ECG) data afterwards.
This hair can be understood and applied the above description of the embodiments is intended to facilitate those skilled in the art
It is bright.Person skilled in the art obviously easily can make various modifications to above-described embodiment, and described herein
General Principle is applied in other embodiment without having to go through creative labor.Therefore, the present invention is not limited to the above embodiments,
Those skilled in the art's announcement according to the present invention, the improvement made for the present invention and modification all should be in the protections of the present invention
Within the scope of.
Claims (5)
1. a kind of intelligent terminal for removing electrocardiosignal vehicle movement noise, including processor and bluetooth communication unit；Its
It is characterized in that, the processor is loaded with following functions module：
Signal acquisition module, for collecting the electrocardio number that cardioelectric monitor device provides by the bluetooth communication module in intelligent terminal
Training data and 3-axis acceleration sensor under each Heart Rate States of correspondence provided according to, electrocardiosignal simulative generator provide
Exercise data；The electrocardiogram (ECG) data is the daily triple channel detected by cardioelectric monitor device in vehicle movement of user
ECG detecting signal segment；The training data include electrocardiosignal simulative generator generated when static be in any heart rate
The static electrocardiosignal section of m group triple channels under state and the m groups under any Heart Rate States generated in vehicle movement
Triple channel exercise electrocardiogram signal section, m are the natural number more than 1；The exercise data includes that 3-axis acceleration sensor synchronizes
Corresponding three axis of the generated triple channel exercise electrocardiogram signal section in vehicle movement adds with electrocardiosignal simulative generator for acquisition
Speed signal section and corresponding with the triple channel ECG detecting signal segment that cardioelectric monitor device is detected in vehicle movement
3-axis acceleration signal segment；
Preprocessing module, for the static electrocardiosignal section of triple channel, triple channel exercise electrocardiogram signal section, the triple channel heart
Power detection signal section and 3-axis acceleration signal segment are pre-processed；Simultaneously for any Heart Rate States, make the Heart Rate States
Under pretreated triple channel exercise electrocardiogram signal section it is corresponding with the static electrocardiosignal section of triple channel make it is poor, obtain m group triple channels
Electrocardio motion artifacts；
Neural metwork training module, for any Heart Rate States, according to the triple channel electrocardio motion artifacts under the Heart Rate States
And pretreated 3-axis acceleration signal segment is trained by artificial neural network learning algorithm, obtains being directed to the heart rate
Vehicle movement noise model of the state about three lead electrocardiosignals；
Denoising module accelerates three axis corresponding with triple channel ECG detecting signal segment for the Heart Rate States residing for user
Degree signal segment inputs output in corresponding vehicle movement noise model and obtains a set of three links road electrocardio motion artifacts, and then makes described
Triple channel ECG detecting signal segment subtract the triple channel electrocardio motion artifacts and be eliminated the threeway after vehicle movement noise
Road electrocardiogram (ECG) data；
The preprocessing module examines the static electrocardiosignal section of triple channel, triple channel exercise electrocardiogram signal section, triple channel electrocardio
It includes format conversion and normalized to survey signal segment and 3-axis acceleration signal segment and carry out pretreatment, with obtain appropriate format and
Data-signal in range；Wherein normalized is based on following formula：
Wherein：X is the either signal value in signal segment, xminAnd xmaxMinimum value and maximum value respectively in signal segment, x' are
Signal values of the corresponding x after normalized；
The detailed process that the neural metwork training module is trained by artificial neural network learning algorithm is as follows：
(1) by m groups 3-axis acceleration signal segment corresponding with triple channel exercise electrocardiogram signal section after pretreatment be divided into training set and
Test set and training set are more than test set；
(2) one neural network being made of input layer, hidden layer and output layer of initialization structure；
(3) appoint from training set and one group of above-mentioned neural computing of 3-axis acceleration signal segment substitution is taken to obtain corresponding to about electrocardio
The output of motion artifacts is made an uproar as a result, calculating the output result with the triple channel electrocardio movement corresponding to the 3-axis acceleration signal segment
Accumulated error between sound；
(4) according to the accumulated error by gradient descent method in neural network between input layer and hidden layer and hidden layer with
Weight between output layer is modified, so appoint from training set remove one group of 3-axis acceleration signal segment substitute into it is revised
Neural network；
(5) according to all 3-axis acceleration signal segments in step (3) and (4) traversal training set, accumulated error minimum when institute is taken
Corresponding neural network is vehicle movement noise model；
The vehicle movement noise model that the neural metwork training module obtains training accelerates three axis in test set
Degree signal segment substitutes into the vehicle movement noise model and obtains the corresponding output about electrocardio motion artifacts as a result, making test set one by one
In output result corresponding to each group of 3-axis acceleration signal segment be compared with triple channel electrocardio motion artifacts, if test set
In there is the comparison result of a certain proportion of 3-axis acceleration signal segment to be less than or equal to threshold value if, then the vehicle movement noise model
It is final to determine；Otherwise it is quiet to acquire more triple channels using electrocardiosignal simulative generator and 3-axis acceleration sensor
Only electrocardiosignal section, triple channel exercise electrocardiogram signal section and 3-axis acceleration signal segment are through the signal acquisition module in intelligent terminal
Input with scale as neural metwork training module to increase training set after preprocessing module is trained.
2. intelligent terminal according to claim 1, it is characterised in that：People used by the neural metwork training module
Artificial neural networks learning algorithm is with ratio conjugate gradient method direction as an optimization.
3. intelligent terminal according to claim 1, it is characterised in that：The neural metwork training module initialization structure
Neural network in input layer be made of 3 neurons, hidden layer is made of 10 neurons, and output layer is by 3 neural tuples
At.
4. intelligent terminal according to claim 1, it is characterised in that：The neural metwork training module initialization structure
Neural network in neuron function g (z) expression formula it is as follows：
Wherein：Z is argument of function.
5. intelligent terminal according to claim 1, it is characterised in that：The intelligent terminal is smart mobile phone, tablet electricity
Brain or PC machine.
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| LU100925B1 (en) * | 2018-09-10 | 2020-03-10 | Iee Sa | Removing noise caused by vehicular movement from sensor signals using Deep Neural Networks |
| CN113491513B (en) * | 2020-04-08 | 2023-06-30 | 华为技术有限公司 | Heart rhythm detection control method and terminal |
| CN112089412A (en) * | 2020-04-17 | 2020-12-18 | 中国人民解放军第二军医大学 | Method for performing algorithm denoising by using motion sensor in cooperation with electrocardiogram monitoring |
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| CN104665849A (en) * | 2014-12-11 | 2015-06-03 | 西南交通大学 | Multi-physiological signal multi-model interaction-based high-speed railway dispatcher stress detecting method |
| CN105246400A (en) * | 2013-03-15 | 2016-01-13 | 佐尔医药公司 | Ecg noise reduction system for removal of vehicle motion artifact |
| WO2016024081A1 (en) * | 2014-08-13 | 2016-02-18 | Saumarez Richard C | Improvement to analysing physiological electrograms |
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| CN105246400A (en) * | 2013-03-15 | 2016-01-13 | 佐尔医药公司 | Ecg noise reduction system for removal of vehicle motion artifact |
| WO2016024081A1 (en) * | 2014-08-13 | 2016-02-18 | Saumarez Richard C | Improvement to analysing physiological electrograms |
| CN104665849A (en) * | 2014-12-11 | 2015-06-03 | 西南交通大学 | Multi-physiological signal multi-model interaction-based high-speed railway dispatcher stress detecting method |
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