CN107184204B - Extraction and expression method of pain component in brain wave - Google Patents
Extraction and expression method of pain component in brain wave Download PDFInfo
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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
- A61B5/369—Electroencephalography [EEG]
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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
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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
- A61B5/48—Other medical applications
- A61B5/4824—Touch or pain perception evaluation
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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
- A61B5/72—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
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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
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
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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
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7271—Specific aspects of physiological measurement analysis
- A61B5/7282—Event detection, e.g. detecting unique waveforms indicative of a medical condition
Abstract
The invention discloses a method for extracting and expressing pain components in brain waves, which is a method for extracting characteristic components related to pain, analgesia, anxiety, tension, delusions, forgetfulness and comfortable feelings in brain waves by collecting brain wave signals in real time and utilizing a mathematical calculation algorithm of discrete signal processing, and expresses the characteristic components into a 0-100 dimensionless data expression mode. Collecting brain wave signals by using an electrophysiological signal sensing technology, applying calculation analysis to the signals, finding and extracting the characteristic indexes, and obtaining quantitative values of the measured characteristic indexes through normalization processing to form a technical means and an application device for real-time pain measurement. The invention provides a powerful support means for scientific research of pain treatment and management methods, and has important practical values in the aspects of analyzing the curative effect of medicaments, summarizing treatment methods, adjusting treatment schemes, researching and analyzing pain mechanisms, collecting large-sample long-time clinical pain related information and the like.
Description
Technical Field
The invention relates to an extraction method and an expression method of brain wave characteristic indexes of pain components, which can be an all-in-one machine or a wireless Internet of things platform, and can be applied to the realization of a household and hospital integrated pain measurement method.
Background
The brain science is the leading science of people's key attention in the modern times. In brain science, the development of brain state measurement technology is an important part of the brain science, and is also one of the targets of brain science application and research. In the field of clinical medicine, until now, no measurement method capable of objectively and quantitatively measuring brain states of pain, analgesia, anxiety, tension, delusions, forgetfulness, comfort and the like in real time is applied to practice. Due to the lack of the real-time objective quantitative measurement technology, in clinical pain treatment, people can measure pain only by relying on a subjective scale of pain, and the subjective scale is used for self-judgment of people subjectively and is in 0-10 scales to describe the pain degree of the people. Such methods are fraught with uncertainty and unrepeatability, leading to inaccurate measurements of pain-related therapeutic effects, unmeasurable levels of analgesia (pain threshold), and inability to achieve objective and moderate results for use of drugs. In particular, anxiety, stress, delirium, amnesia with pain are more unmeasurable and corresponding treatments are applied. Secondly, the internet technology and the communication technology are one of the supporting forces leading scientific and technical progress, and the integration and exchange of medical information are realized on an internet platform by a wireless communication means in combination with clinical medical services, so that for chronic pain patients, such as cancer pain patients, the analgesia treatment in a home environment is not sufficient, real-time objective quantitative pain indexes are not available, doctors cannot apply medicine treatment according to the objective pain degree of the patients, and the patients are usually killed in extreme pain. For acute pain patients, such as postoperative pain treatment, quantitative data of brain states including pain depth are needed as objective basis for doctors to treat pain, and the development of internet technology provides a technical means which can be applied for doctors to know pain, analgesia, anxiety, tension, delusions, forgetfulness and comfort degree of patients at home or on hospital beds in real time.
The establishment of an information management platform for acute and chronic pain treatment and monitoring based on internet communication can provide a powerful support means for scientific research of pain treatment and management methods. Especially, the method has important practical values in the aspects of analyzing the curative effect of the medicament, summarizing the treatment method, adjusting the treatment scheme, researching and analyzing the pain mechanism, collecting large-sample long-time clinical pain related information and the like for the grasp of quantitative data of pain, analgesia, anxiety, tension, delusions, forgetfulness and comfort degree of a patient.
Disclosure of Invention
The invention aims to provide a method for extracting and expressing pain components in brain waves, which aims to solve the technical problems of extracting the characteristic components of pain, analgesia, anxiety, tension, delirium, forgetfulness and comfort degree in the brain wave signals by acquiring the brain wave signals, forming pain measuring equipment and realizing the application of pain management in clinical medical service and household health management.
In order to achieve the above purpose, the technical method adopted by the invention is as follows:
an extraction and expression method of pain components in brain waves comprises extraction methods and expression modes of characteristic components related to pain, analgesia, anxiety, tension, delusions, forgetfulness and comfort in brain waves of a human body caused by pain in various environmental states; the brain waves are used as an original signal processing unit, discretized and collected into a computer, mathematical calculation analysis is applied, the brain waves are decomposed into low-frequency, medium-frequency, high-frequency and ultrahigh-frequency units, characteristic components related to pain, analgesia, anxiety, tension, delusions, forgetfulness and comfort are extracted, and the characteristic components are expressed as dimensionless data of 0-100 after normalization processing, so that real-time objective quantitative depth of pain, analgesia, anxiety, tension, delusions, forgetfulness and comfort of a person is reflected in real time; the brain electric signal acquisition system is characterized in that an electroencephalogram sensor is worn on the forehead and ear parts of the head to noninvasively acquire electric potential signals of a plurality of parts of the brain, the signals enter a calculation unit through a preamplifier, a multistage integrated circuit and an analog-to-digital converter, the signal bandwidth comprises ultrahigh frequency components exceeding more than 30hz in brain waves, electroencephalogram acquisition leads are set to be two leads at least comprising the forehead leaves of the left brain and the right brain, the two leads enter a single-chip computer system through preamplifier and analog-to-digital conversion, the two leads are encrypted and compressed through data and then are directly transmitted to the single-chip computer system in a wired mode or are packaged and transmitted to an internet data server through a TCP/IP protocol of a wireless communication control unit, and a software system in the computer or the server processes, calculates, displays, stores and forwards the received electroencephalogram signals in real time, wherein the calculation part adopts wavelet, The waveform recognition algorithm decomposes various effective components and pseudo-differential components related to frequency and time domains in brain waves, combines the effective components and pseudo-differential components into a new data stream, extracts characteristic indexes of pain, analgesia, anxiety, tension, prospective delusions, forgetfulness and comfort through fuzzy recognition analysis, fitting regression analysis, correlation analysis and multi-dimensional spectrum analysis, obtains a plurality of quantitative data from 0 to 100 through normalization processing, objectively and quantitatively reflects the degree of pain, analgesia, anxiety, tension, prospective delusions, forgetfulness and comfort of a human body in real time, and obtains the indexes of pain depth, analgesia depth, anxiety depth, tension depth, prospective delusions depth, forgetfulness depth and comfort depth.
The electroencephalogram signal acquisition unit has wired and wireless communication capabilities, can be combined with a computer to form an integrated pain depth measurement system, can also be combined with an internet platform and a server to form a cloud computing mode pain depth measurement system to form a remote medical service sharing platform for pain measurement data and a new pain measurement big data collection mode,
the method comprises the steps of utilizing a 232 interface standard, a 3G, 4G, Bluetooth or WIFI communication technology to achieve near-remote multi-environment real-time electroencephalogram acquisition and transmission terminals, achieving analog-to-digital conversion discretization processing on acquired and amplified electroencephalogram analog signals through a single-chip computer, compressing, encrypting and packaging multi-path electroencephalogram discrete signals in real time, achieving dynamic storage queue management, identifying communication lines, automatically switching communication modes, controlling communication transmission, transmitting real-time data packets or calculation processing directly transmitted into the computer through the 232 communication interface, and displaying formed pain depth indexes, analgesia depth indexes, anxiety depth indexes, tension depth indexes, delirium depth indexes, forgetting depth indexes and comfortable depth indexes on a computer terminal or a mobile phone end.
Pain values are associated with quantitative measurement of pain easing, anxiety, tension, delirium, forgetting and comfortable psychological mental states, quantitative expressions of pain easing, anxiety, tension, prospective delusions, forgetting and comfort are synchronously extracted from brain waves, and the quantitative expressions of pain depth are combined to complete a pain measurement process, so that the completeness and accuracy of pain measurement are achieved.
In the wavelet processing calculation, the two bioelectricity signal sequences can be decomposed and used as the signal intensity and the marking characteristics of interference signals;
adopting a wavelet formula:
a, tau, which is the scaling factor and the translation parameter of the wavelet transform;
ω, the angular rating of the wavelet transform;
Ψ(aω) Is the mother function of the wavelet transform;
x (w) is a wavelet transform result sequence of the original brain wave discrete sequence;
WTx is the time domain signal sequence after inverse transformation, i.e. the final result sequence;
for electroencephalogram vector group
bi(t)＝[x1 x2 x3…xm-2 xm-1 xm]
i: number of brain wave leads, m: number of vector elements, x: electroencephalogram data, t: point in time
Real-time computing, decomposing wavelet basis function under each scale window by multi-scale filter bank algorithm
(Wb(2^j，wj(x)))j∈z
(Wb(2^0，w0(x)))，(Wb(2^1，w1(x)))…(Wb(2^N，wN(x)))
wj(x) Representing a set of resulting data sequences after wavelet transform, x representing a sequence number in the data sequence, z: a time domain space;
j: wavelet basis number (dimension number)
A group of time domain reconstruction functions are obtained by inverse transformation of wavelet basis functions and data of all scales as follows:
fi(t)＝∑Wb(2^i,wi)*Ψ2^i(x)
i: dimension
Ψ 2^0(x), Ψ 2^1 (x.. Ψ 2^ N (x): mother function scale wavelet data points
N: the order;
each reconstruction function represents the performance of brain waves, eye movement waves and muscle waves under different scales; the scale also corresponds to the frequency component of the signal, distributed in the conventional rhythm and high-frequency rhythm of the electroencephalogram; for each sequence data of the decomposed reconstruction function, extracting the characteristic points of the data by adopting a waveform recognition algorithm in a pattern recognition algorithm:
Tj(x)∈z；
t: eigenvalue vector, j: dimension, x: discrete data representing waveform characteristics; z: a time domain space;
vector Tj(x) The characteristic data in (2) comprises calculation results of specific points, amplitude, variation, slope, area, autocorrelation and cross-correlation, and the calculation is from a basic algorithm:
data sequence:
y(t，i)＝(fi(j)-fi(j-1))/Δt
j: discrete data subscript, i: dimension;
obtaining the maximum value in the sequence y (t, i) to obtain one of the characteristic indexes, wherein the positive and negative inversion points are special points, and the number of the special points is represented by the value of t;
to fi(x) The sequence data applies an iterative differential algorithm:
d(j，k)＝∑(fi(j+k)-fi(j+k-1)/(Δt+k))
k: delta of Δ t, from 1.. N, j: numerical serial numbers; i: dimension
For each vector in the matrix d (j, k), sorting and adding data points in the vector, and selecting the maximum sum in each vector as a slope and an amplitude;
for each scale vector f in the reconstruction functioni(x)＝[x1 x2 x3…xm-2 xm-1 xm]Generating mode types omega 1, omega 2, … and omega c from the processed result vectors y (t, i) and d (j, k), and then calculating the distance of each reconstruction function by using the distance function between the modes; obtaining the variation, autocorrelation and cross-correlation values of the reconstruction function;
calculate the integral of the reconstruction function:
Si(x)＝∫fi(x)*Δx
obtaining the area value of each function;
Tj(x) The vector expresses the waveform characteristics and rules of the reconstruction function; for the acquired eye movement electric signals and frontal muscle electric signals in the brain waves, the eye movement electric signals and the frontal muscle electric signals are distributed in reconstruction functions with specific scales, and for the reconstruction functions, first, a first derivative of the reconstruction functions is obtained:
Di(x)＝(fi(x)-fi(x+m))/Δx
x: abscissa, Δ x: an abscissa increment;
to Di(x) Sorting to obtain maximum and minimum values, setting threshold Q (setting constant), and obtaining
Di(x) Obtaining a group of extreme point vectors by the positive and negative polarity change points:
Mi(j) (ii) a A high point;
mi(j) (ii) a A low point;
j: the number of extreme points;
and (3) adopting an integral algorithm for the correlation reconstruction function:
E＝∫fi(t)^2*Δt
obtaining the power values of myoelectricity and eye movement; aiming at the result data, through a normalization combination algorithm:
Sq＝exp(a*abs(Mi-mi)+b*E)；
a, b: weighting coefficients determined by the signal expression ranges;
a quantitative expression Sq indicating the signal intensity of the interference can be obtained as one of the display results;
eliminating the brain wave wavelet reconstruction component of eye movement and myoelectricity in the reconstruction function, adopting a power spectrum algorithm:
fi(x) The method comprises the following steps Brain wave wavelet reconstruction function, x (w): the magnitude of the spectral power;
various components of the power spectrum in brain waves, including the values of α β δ θ band, sef, mef edge frequency, dominant frequency value:
Fi＝{α，β，δ，θ…sef,mef}；
combining the feature vectors obtained by waveform identification to form a group covering time domain and frequency domain
Gi(x)＝{Tij，Fij}；
i: dimension; j: the serial number of the characteristic value.
Data vector Gi(x) A set, which is a primary processing result of the brain wave wavelet reconstruction function, named as a metadata set of brain wave primary processing, may be used as basic data of secondary calculation;
extracting characteristic data in the reconstruction function metadata of each scale, and calculating the ratio of data corresponding to each scale, the slope, the change rate and the integral area of the data of each scale:
Yj(x，t)＝{Gi(x，t)-Gi(x，t-1)/Δt}∈z；
Zj(x)＝{Gi(x1)/Gj(x1)}∈z；
Sj(x，t)＝{∫Gi(x，t)*Δt}∈z；
Lj(x，t)＝{abs(Gj(x，t1)-Gj(x，t2))}∈z；
t: calculating the time point, i, j: scale sequence, x: a feature vector subscript; z: a time domain computation space;
and for each data sequence, obtaining a series of data calculation formulas through data weighting:
Ei＝{a，b，c，d…}*{Yj，Zj，Sj，Lj}；
i: a data sequence number; a. b, c, d: a weighting coefficient;
applying a normalization calculation to the Ei data:
pain index ═ 100 (exp (E0)) ×
Analgesic index ═ 100 (exp (E1)) ×
Stress index (exp (E2)) × 100
Delirium index (exp (E3)) × 100
Anxiety index ═ 100 (exp (E4)) ×
Forgetting index ═ 100 (exp (E5)) ×
Comfort index ═ 100 (exp (E6));
the real-time objective quantitative characteristic indexes of pain, analgesia, anxiety, tension, delusions, forgetfulness and comfort in brain waves are obtained.
The invention has the advantages that:
the invention extracts the characteristic components related to pain, analgesia, anxiety, tension, delusion, forgetfulness and comfortable feeling in brain waves by collecting brain wave signals in real time and utilizing a mathematical calculation algorithm of discrete signal processing, and expresses the characteristic components into an expression mode of 0-100 dimensionless data. Collecting brain wave signals by using an electrophysiological signal sensing technology, applying various mathematical calculation algorithms to the signals, finding and extracting characteristic indexes of pain, analgesia, anxiety, tension, delusion, forgetfulness and comfort components in the signals, and obtaining quantitative values of the characteristic indexes through normalization processing to form a technical means and an application device for real-time pain measurement. The invention provides a powerful support means for scientific research of pain treatment and management methods, and has important practical values in the aspects of analyzing the curative effect of medicaments, summarizing treatment methods, adjusting treatment schemes, researching and analyzing pain mechanisms, collecting large-sample long-time clinical pain related information and the like.
Drawings
Fig. 1 is a connection relationship between parts of the mobile information acquisition and transmission device of the present invention.
Detailed Description
The principle and structure of the present invention are shown in fig. 1. An extraction and expression method of pain components in brain waves comprises extraction methods and expression modes of characteristic components related to pain, analgesia, anxiety, tension, delusions, forgetfulness and comfort in brain waves of a human body caused by pain in various environmental states; the brain waves are used as an original signal processing unit, are discretely collected into a computer, are applied with various mathematical calculation algorithms, are decomposed into low-frequency, medium-frequency, high-frequency and ultrahigh-frequency units, characteristic components related to pain, analgesia, anxiety, tension, delusions, forgetfulness and comfort are extracted, and are expressed as dimensionless data of 0-100 through normalization processing, so that real-time objective quantitative depth of pain, analgesia, anxiety, tension, delusions, forgetfulness and comfort of a human is reflected in real time;
the method comprises the steps of utilizing an electroencephalogram sensor to be worn on the forehead and the ear of a head, noninvasively acquiring electric potential signals of a plurality of parts of the brain, enabling the signals to enter a computing unit through a preamplifier, a multistage integrated circuit and an analog-to-digital converter, enabling signal bandwidth to comprise ultrahigh frequency components (brain wave components more than 30 hz) in brain waves, enabling electroencephalogram acquisition leads to be set to be two leads at least comprising the left and right brain forehead leaves, enabling the two leads to enter a single-chip computer system through preamplifier and analog-to-digital conversion, enabling the two leads to be directly transmitted to the single-chip computer system in a wired mode after data encryption and compression, or enabling the two leads to be packaged and transmitted to an internet data server through a TCP/IP protocol of a wireless communication control unit, enabling a special software system in the computer or the server to process, display, store and forward the received electroencephalogram signals in real time, wherein, The waveform recognition algorithm decomposes various effective components and pseudo-differential components related to frequency and time domains in brain waves, combines the effective components and pseudo-differential components into a new data stream, extracts characteristic indexes of pain, analgesia, anxiety, tension, delusions, forgetfulness and comfort through various algorithms such as fuzzy recognition analysis, fitting regression analysis, correlation analysis, multi-dimensional spectrum analysis and the like, obtains a plurality of quantitative data from 0 to 100 through normalization processing, objectively and quantitatively reflects the degree of pain, analgesia, anxiety, tension, delusions, forgetfulness and comfort of a human body in real time, and obtains the pain depth, the analgesia depth, the anxiety depth, the tension depth, the delusions depth, the forgetfulness depth and the comfort depth index.
The electroencephalogram signal acquisition unit has wired and wireless communication capabilities, can be combined with a computer to form an integrated pain depth measurement system, can also be combined with an internet platform and a server to form a cloud computing mode pain depth measurement system to form a remote medical service sharing platform for pain measurement data to form a new pain measurement big data collection mode, utilizes 232 interface standard, 3G, 4G, Bluetooth and WIFI communication technology to realize near-remote, multi-environment and real-time electroencephalogram acquisition and transmission terminals, and realizes discretization processing of analog-to-digital conversion on the acquired and amplified electroencephalogram analog signals by a single-chip computer, compresses, encrypts and packs multi-path electroencephalogram discrete signals in real time to realize dynamic storage queue management, identifies communication lines, automatically switches communication modes, controls communication transmission, and directly transmits real-time data packets into the computer (by using a 232 communication interface) or passes through the wireless internet platform, the pain depth index, the analgesia depth index, the anxiety depth index, the tension depth index, the delirium depth index, the forgetting depth index and the comfort depth index which are formed by the calculation processing of the computer or the cloud computing server are directly sent to the cloud computing server and displayed on a computer terminal or a mobile phone end.
Pain values are associated with quantitative measurement of pain easing, anxiety, tension, delirium, forgetting and comfortable psychological mental states, quantitative expressions of pain easing, anxiety, tension, delusions, forgetting and comfort are synchronously extracted from brain waves, and the quantitative expressions of pain depth are combined to complete the pain measurement process, so that the integrity and accuracy of pain measurement are achieved; pain is a manifestation of brain state, pain relieving, anxiety, tension, delirium, amnesia, comfort and also a manifestation of brain state, and the pain can cause anxiety, tension, delirium and amnesia of people; anxiety and tension have the effect of enhancing the degree of pain, and particularly in tension and anxiety states, the threshold of pain (analgesia) suffered by people is reduced; the level of anxiety, stress, delirium, amnesia, comfort can be associated with pain, or can occur in the absence of pain.
The invention relates to a brain wave characteristic component extraction method and a normalization expression mode, wherein the brain wave characteristic component extraction method comprises a module or a piece of equipment, a mobile information acquisition and transmission terminal, a related mobile communication terminal, a fixed terminal, calculation and data management software and the like, and the brain wave characteristic component extraction method comprises a multi-lead brain wave acquisition and processing part, a non-invasive electrode, a communication control module, a computer and a server, and has pain, analgesia, anxiety, tension, delusion, forgetfulness and comfort degree.
The mobile information acquisition and transmission equipment comprises a preamplifier circuit, a singlechip control circuit, a dynamic data link cache circuit, a combined wireless communication access control circuit, a power supply circuit and the like.
The multi-lead brain wave signal of the patient is collected and transmitted to the filtering, noise controlling and amplifying input part of the preamplifier circuit through the brain wave receptor electrode (bioelectric signal sensor) on the patient, and is respectively transmitted to the analog-digital conversion circuit of the singlechip control part of the terminal through corresponding channels. The single chip computer control part obtains digitized brain wave (including high-frequency brain wave over 30 hz) data, and the data is encrypted and compressed to obtain processed data flow which is sent to a dynamic data link cache queue. A dynamic data link buffer queue is a changing data storage and output structure. The structure of data in the storage area is different according to the network state. The singlechip control circuit controls different permutation and combination of the acquired data after acquiring the triggering of the interruption event of the network state change. And writing the data in the storage queue under the control of the write command. And the data in the queue is output to the Bluetooth, wifi and 3G, 4G internet communication modules through the data port under the control of the reading instruction of the single chip control circuit. The internet communication module completes the functions of automatic dialing, network state identification, TCP mode signal modulation, subpackage and output of the network. The acquired multi-lead brain electrical data can be directly sent into a computer in a wired or wireless mode, and simultaneously, automatically transmitted into a cloud computing server through an internet platform.
The data center server manages data sent by all the mobile information acquisition and transmission terminals, each terminal sets a unique address code and consists of a machine number, a server number and a fixed IP address of the server:
the terminal machine address is the unique address number in the range of the server number + the fixed IP address of the networking server
65535 maximum machine number in the network
The mobile information acquisition and transmission terminal carries out encryption processing and compression on the data converted into the digital signals. The integrated data stream enters a link store queue. The data window is 7-15 beta
The L stream window is m + addr + asyn + data
m is an encryption mark transmitted by the module, addr is a machine address, asyn is synchronization, and data is encryption data comprising numerical data and waveform data
Terminal data transmission, status reception, and command reception are fundamental guarantees for system implementation. And is also one of the key links for safe use of the system. When a computer in a short distance is used as a processing platform, the terminal data directly receives the data through a wifi or Bluetooth communication port configured on the computer, and an operating system and pain measurement application software of the computer complete all work of data calculation, display and storage. Meanwhile, the terminal sends the original brain wave data to an internet platform, in a set cloud processing server chain, the data is received, calculated, stored and displayed by a corresponding server provided with special webserver application data flow control management software, and the result data is sent to the internet platform in the same encryption format for relevant personnel to share on different terminals at different geographic positions. The GPRS or 3G, 4G communication technology is not a completely stable wireless communication platform in the mobile. Many links can affect the effectiveness of data transfer. For the transmission of numerical data, UDP packets may be used. And a multi-time sending mode is adopted, so that the server can receive an effective data entity once. Continuity and exclusivity are required for transmission of waveform images. The transmission of the waveform image must employ TCP packets. And the flow control capability of the single chip computer is utilized to process the data transmission state and the internal buffer size in real time. The real-time process of network communication is automatically identified and self-corrected, and then the direction of data flow and network call dialing are controlled. Up to six minutes of data link dynamic queue area is set. The integrity of data and the improvement of fault-tolerant capability are ensured.
Data waits for network access in the link memory queue. The network state trigger circuit triggers a network state event, and the event processing flows of the calculation control unit respectively enter different thread processing units according to the event properties. Under normal state, obtaining link data, and sending protocol packet to network address and port set in network through wireless network by TCP protocol packet.
TCP protocol package + (save _ point + +) real _ data → save _ point + +
save _ point is the data address pointer and real _ data is the real-time data.
The dynamic data link management comprises memory data queue management, data encryption management and data compression management. Covers the function of dynamic mutual coordination of current data transmission.
The data transmission of the system adopts a public network and a wireless mode, the encryption of the data adopts a method that a data structure is dynamically changed along with the data content, and one of the changing rules is as follows: the positions of the leads in the data stream are automatically arranged according to the dynamic size of the lead values. Secondly, a 16-bit binary code weighting algorithm is adopted to perform weighting calculation on the data stream in real time, and an encrypted data stream is generated and sent to the internet platform. Thirdly, the data stream does not contain the personal identity information of the user, and the corresponding information is bound in the use login system of the central workstation.
For mobile application, real-time data of each mobile information acquisition and transmission terminal is firstly transmitted to a certain server in a cloud computing server chain appointed by a terminal address, and the server runs a special cloud computing processing real-time software package, a data forwarding management software package and a user identity synchronous management software package. The server completes intelligent information processing monitoring software for data calculation, identity matching, original and result data storage, forwarding, command generation and the like. The server cloud computing extracts quantitative expressions of brain states such as pain, analgesia, anxiety, tension, delusions, forgetfulness, comfort and the like in real time, and intelligent terminals such as computers, mobile phones and mobile panels distributed in different places are responsible for real-time data display and alarm. The server needs to connect with the public internet and requires a fixed IP address to be configured on the router. The server can be interconnected with an intranet of a hospital, a VPN channel of the intranet and the intranet is established, calculation result information enters the intranet of the hospital under the protection of multiple firewalls, computers (including desktop computers, mobile phones, tablet computers and the like) connected with the intranet of the hospital can be used as data display terminals, and software services entering the system are controlled by the authority of users. A user identity management server is configured in the cloud computing server link and is used as an identity registration workstation of a user (including a doctor, a spotter and a patient), and the server is usually arranged in an intranet of a hospital. The software binds the data acquisition transmission terminal address with the user identity and allocates the authority. The measured result data of pain, analgesia, anxiety, tension, delusions, forgetfulness and comfortable depth of all the using processes and all the information of the time, the identity and the like of the user are uniformly stored in the corresponding databases.
For non-mobile application occasions of medical real-time monitoring, in order to meet the requirement of timely data display expression, the data processing mode that data are uploaded to an internet platform in real time is adopted, and meanwhile, data exchange is realized through a wifi or Bluetooth communication module configured on the terminal and wifi and Bluetooth communication interfaces of close-range computers (including desktop computers, flat panels and the like). The data calculation local version software on the computer carries out calculation processing on the data, and the obtained result is displayed on a computer screen, thereby completing the application of the traditional functional mode of the medical monitor. Meanwhile, cloud computing results are synchronously transmitted to mobile phones of medical staff, and the application mode of the portable mobile phone monitor is realized.
Medical staff's personal computer, cell-phone can directly acquire different patients' painful information of diagnosing under corresponding authority through internet, and the special software package is installed to the personal computer, and the cell-phone needs to download dedicated APP application software. The authority management is to be classified into the overall management authority range of the hospital. Meanwhile, all operation records are sent back to the cloud computing server and are stored as original data for tracking, mining and analyzing. The real-time online interaction between the doctors and the patients and between the first-line and second-line doctors is realized by the computers or the personal mobile communication terminals connected to the server end, and the communication information is bidirectionally forwarded by the server to achieve the purpose of real-time bidirectional communication. The real-time interaction function is realized by the system software function.
The wireless communication control access module adopted by the data acquisition and transmission terminal can be replaced by a mobile phone, and the mobile phone receives the encrypted data stream sent by the terminal under the support of the pain measurement application APP software through the Bluetooth function configured on the mobile phone. And the data is synchronously transmitted to the Internet platform in real time through the processes of displaying, calling and forwarding. The method comprises the steps of setting Bluetooth communication between a mobile phone and a terminal, pairing, adopting a Socket thread in a mobile phone APP, initializing a Bluetooth channel, entering a receiving flow, decoding a received data signal, and displaying the decoded data signal on a mobile phone screen in real time. And simultaneously, for the decoded data, extracting the cloud computing server address and the port address, sending the received original data to the corresponding cloud computing server without any processing, receiving command data and result data sent by the corresponding server by the APP through another Socket thread, wherein the decryption computing format of the data is the same as the data computing format sent by the terminal. For the result data, the APP displays the data at the fixed screen position of the mobile phone in real time, so that a user or a doctor can observe changes of body states such as pain depth and the like in real time. The downloading of the pain measurement application software requires a user to set corresponding real identity information, the identity card number is used as a unique user identification code, a personal file of the user is established in a user management server during downloading, wherein the personal file contains identity information of a patient and a doctor, if data confidentiality and attribution requirements exist, a server framework can be arranged on an intranet of a hospital, and then the server framework enters an internet platform through a VPN encryption protection channel. Once the user information is generated, the mobile phone APP software can become a data forwarding and result display terminal. The number of handsets that download pain applications is not limited.
For brain wave processing, calculating different component sequences of the brain and time domain characteristics and multi-scale complexity characteristics of each sequence by adopting waveform identification and wavelet analysis algorithms through discretization processing with the sampling frequency of 1400/s, the sampling time window of 1.25s and the sampling precision of 10-bit. And extracting rhythm components in the brain waves and the power of each rhythm wave band by adopting a power spectrum algorithm to obtain a calculation result sequence of primary processing calculation, wherein the sequence corresponds to hundreds of independent or weakly related regular characteristics in the brain waves. The brain waves collected from both sides of the forehead include an eye movement electric component and a frontal muscle electric component. In wavelet processing calculation, the two bioelectric signal sequences can be decomposed and used as signal intensity and a marking characteristic of an interference signal.
Adopting a wavelet formula:
a, tau, which is the scaling factor and the translation parameter of the wavelet transform;
ω, the angular rating of the wavelet transform;
Ψ(aω) Is the mother function of the wavelet transform;
x (w) is a wavelet transform result sequence of the original brain wave discrete sequence;
WTx is the time domain signal sequence after inverse transformation, i.e. the final result sequence;
for electroencephalogram vector group
bi(t)＝[x1 x2 x3…xm-2 xm-1 xm]
i: number of brain wave leads, m: number of vector elements, x: electroencephalogram data, t: point in time
Real-time computing, decomposing wavelet basis function under each scale window by multi-scale filter bank algorithm
(Wb(2^j,wj(x)))j∈z
(Wb(2^0,w0(x)))，(Wb(2^1,w1(x)))…(Wb(2^N,wN(x)))
wj(x) Representing a set of resulting data sequences after wavelet transform, x representing a sequence number in the data sequence, z: a time domain space;
j: wavelet basis number (dimension number)
A group of time domain reconstruction functions are obtained by inverse transformation of wavelet basis functions and data of all scales as follows:
fi(t)＝∑Wb(2^i,wi)*Ψ2^i(x)
i: dimension
Ψ 2^0(x), Ψ 2^1 (x.. Ψ 2^ N (x): mother function scale wavelet data points
N: the order;
each reconstruction function represents the performance of brain waves, eye movement waves and muscle waves under different scales; the scale also corresponds to the frequency component of the signal, distributed in the conventional rhythm and high-frequency rhythm of the electroencephalogram; for each sequence data of the decomposed reconstruction function, extracting the characteristic points of the data by adopting a waveform recognition algorithm in a pattern recognition algorithm:
Tj(x)∈z；
t: eigenvalue vector, j: dimension, x: discrete data representing waveform characteristics; z: a time domain space;
vector Tj(x) The characteristic data in (2) comprises calculation results of specific points, amplitude, variation, slope, area, autocorrelation and cross-correlation, and the calculation is from a basic algorithm:
data sequence:
y(t，i)＝(fi(j)-fi(j-1))/Δt
j: discrete data subscript, i: dimension;
obtaining the maximum value in the sequence y (t, i) to obtain one of the characteristic indexes, wherein the positive and negative inversion points are special points, and the number of the special points is represented by the value of t;
to fi(x) The sequence data applies an iterative differential algorithm:
d(j，k)＝∑(fi(j+k)-fi(j+k-1)/(Δt+k))
k: delta of Δ t, from 1.. N, j: numerical serial numbers; i: dimension
For each vector in the matrix d (j, k), sorting and adding data points in the vector, and selecting the maximum sum in each vector as a slope and an amplitude;
for each scale vector f in the reconstruction functioni(x)＝[x1 x2 x3…xm-2 xm-1 xm]Generating mode types omega 1, omega 2, … and omega c from the processed result vectors y (t, i) and d (j, k), and then calculating the distance of each reconstruction function by using the distance function between the modes; obtaining the variation, autocorrelation and cross-correlation values of the reconstruction function;
calculate the integral of the reconstruction function:
Si(x)＝∫fi(x)*Δx
obtaining the area value of each function;
Tj(x) The vector expresses the waveform characteristics and rules of the reconstruction function; for the acquired eye movement electric signals and frontal muscle electric signals in the brain waves, the eye movement electric signals and the frontal muscle electric signals are distributed in reconstruction functions with specific scales, and for the reconstruction functions, first, a first derivative of the reconstruction functions is obtained:
Di(x)＝(fi(x)-fi(x+m))/Δx
x: abscissa, Δ x: an abscissa increment;
to Di(x) Sorting to obtain maximum and minimum values, setting threshold Q (setting constant), and obtaining
Di(x) Obtaining a group of extreme point vectors by the positive and negative polarity change points:
Mi(j) (ii) a A high point;
mi(j) (ii) a A low point;
j: the number of extreme points;
and (3) adopting an integral algorithm for the correlation reconstruction function:
E＝∫fi(t)^2*Δt
obtaining the power values of myoelectricity and eye movement; aiming at the result data, through a normalization combination algorithm:
Sq＝exp(a*abs(Mi-mi)+b*E)；
a, b: weighting coefficients determined by the signal expression ranges;
a quantitative expression Sq indicating the signal intensity of the interference can be obtained as one of the display results;
eliminating the brain wave wavelet reconstruction component of eye movement and myoelectricity in the reconstruction function, adopting a power spectrum algorithm:
fi(x) The method comprises the following steps Brain wave wavelet reconstruction function, x (w): the magnitude of the spectral power;
various components of the power spectrum in brain waves, including the values of α β δ θ band, sef, mef edge frequency, dominant frequency value:
Fi＝{α，β，δ，θ…sef,mef}；
combining the feature vectors obtained by waveform identification to form a group covering time domain and frequency domain
Gi(x)＝{Tij，Fij}；
i: dimension; j: the serial number of the characteristic value.
Data vector Gi(x) A set, which is a primary processing result of the brain wave wavelet reconstruction function, named as a metadata set of brain wave primary processing, may be used as basic data of secondary calculation;
extracting characteristic data in the reconstruction function metadata of each scale, and calculating the ratio of data corresponding to each scale, the slope, the change rate and the integral area of the data of each scale:
Yj(x，t)＝{Gi(x，t)-Gi(x，t-1)/Δt}∈z；
Zj(x)＝{Gi(x1)/Gj(x1)}∈z；
Sj(x，t)＝{∫Gi(x，t)*Δt}∈z；
Lj(x，t)＝{abs(Gj(x，t1)-Gj(x，t2))}∈z；
t: calculating the time point, i, j: scale sequence, x: a feature vector subscript; z: a time domain computation space;
and for each data sequence, obtaining a series of data calculation formulas through data weighting:
Ei＝{a，b，c，d…}*{Yj，Zj，Sj，Lj}；
i: a data sequence number; a. b, c, d: a weighting coefficient;
applying a normalization calculation to the Ei data:
pain index ═ 100 (exp (E0)) ×
Analgesic index ═ 100 (exp (E1)) ×
Stress index (exp (E2)) × 100
Delirium index (exp (E3)) × 100
Anxiety index ═ 100 (exp (E4)) ×
Forgetting index ═ 100 (exp (E5)) ×
Comfort index ═ 100 (exp (E6));
the real-time objective quantitative characteristic indexes of pain, analgesia, anxiety, tension, delusions, forgetfulness and comfort in brain waves are obtained.
Claims (2)
1. A method for extracting and expressing pain components in brain waves is characterized in that: the brain waves are used as original signals to be processed, discretized and collected into a computer or a server, mathematical calculation analysis is applied, the brain waves are decomposed into components covering low frequency, medium frequency, high frequency and ultrahigh frequency, characteristic components related to pain, analgesia, anxiety, tension, prospective delusions, forgetfulness and comfort are extracted, the components are expressed into dimensionless data of 0-100 through normalization processing, and real-time objective quantitative depth of pain, analgesia, anxiety, tension, prospective delusions, forgetfulness and comfort of a human is reflected in real time; the brain wave sensing electrodes are worn on the forehead and ears of the head, lead potential signals of multiple parts of the brain are collected in a non-invasive mode, the signal bandwidth comprises ultrahigh frequency components exceeding 30Hz in brain waves, the brain wave signals of all leads enter a single chip microcomputer through preamplification and analog-to-digital conversion, the single chip microcomputer directly transmits the signals to a computer system in a wired mode after data encryption and compression, or the signals are packaged and transmitted to an internet data server through a TCP/IP protocol of a wireless communication control unit, and a software system in the computer or the server processes, displays, stores and forwards the received brain wave signals in real time, wherein a calculating part adopts wavelet analysis and a waveform identification algorithm to decompose effective components and pseudo-differential components in the brain waves, and extracts pain through multi-dimensional spectral analysis, mode identification analysis, correlation analysis and fitting regression analysis, Characteristic indexes of analgesia, anxiety, tension, prospective delusions, forgetfulness and comfort are subjected to normalization processing to obtain a plurality of quantitative data of 0-100, and the pain, analgesia, anxiety, tension, prospective delusions, forgetfulness and comfort degrees of a human body are objectively and quantitatively reflected in real time to obtain pain depth, analgesia depth, anxiety depth, tension depth, prospective delusions depth, forgetfulness depth and comfort depth indexes; the electroencephalogram signal acquisition unit has wired and wireless communication capabilities, is combined with a computer to form a pain depth measurement system, or is combined with a server to form a pain depth measurement system in a cloud computing mode through an internet platform, a remote medical service sharing platform for pain measurement data is formed, and a new pain measurement big data collection mode is formed: the method comprises the steps that a 232 interface standard, a 3G, 4G, Bluetooth and/or WIFI communication technology are utilized to achieve near-remote multi-environment real-time electroencephalogram acquisition and transmission, for acquiring and amplifying electroencephalogram analog signals, a single chip microcomputer performs analog-to-digital conversion on the electroencephalogram analog signals, multi-path electroencephalogram discrete signals are compressed, encrypted and packaged in real time, dynamic storage queue management is achieved, communication lines are identified, communication modes are automatically switched, and communication is controlled to send real-time data packets; in the wavelet processing calculation, an eye movement electric wave and myoelectric wave bioelectric signal sequence is decomposed and used as the signal intensity and the marking characteristic of an interference signal;
adopting a wavelet formula:
for electroencephalogram vector group
bi1（t）= [ y'1 y'2 y'3 … y'm’-2 y'm’-1 y'm’ ]
i 1: the number of brain wave leads;
m': the number of brain wave data;
y': a brain wave data sequence;
t: a point in time;
real-time computing, decomposing wavelet basis function under each scale window by multi-scale filter bank algorithm
(Wb(2^i,wi（r）)) i∈z
(Wb(2^0, w0（r）))，(Wb(2^1, w1（r）))…(Wb(2^N, wN（r）))
wi(r): represents a set of resulting data sequences after wavelet transformation;
r: representing a sequence number in the data sequence;
z: a time domain space;
i: dimension;
a group of time domain reconstruction functions are obtained by inverse transformation of wavelet basis functions and data of all scales as follows:
ｆi(t)=∑Wb(2^i,wi)*Ψ2^i(u)
i: dimension
Wb: a wavelet basis function;
wi: the result data after wavelet transform of each scale;
Ψ2^0(u), Ψ2^1(u)... Ψ2^N(u): a mother function scale wavelet data point;
n: the order;
u: a data sequence number of a scale wavelet data point;
t: a point in time;
each reconstruction function represents the performance of brain waves, eye movement waves and muscle waves under different scales; the scale also corresponds to the frequency component of the signal, distributed in the conventional rhythm and high-frequency rhythm of the electroencephalogram; for each sequence data of the decomposed reconstruction function, extracting the characteristic points of the data by adopting a waveform recognition algorithm in a pattern recognition algorithm:
Ti（h）∈ z ；
t: a vector of eigenvalues;
i: dimension;
h: a serial number of the characteristic data;
z: a time domain space;
vector Ti(h) The characteristic data in (2) comprises calculation results of specific points, amplitude, variation, slope, area, autocorrelation and cross-correlation, and the calculation is from a basic algorithm:
calculating a data sequence:
y（j'，i）= （fi（j'）-fi（j'-1））/ Δt'
fi(j') is fi(t) the discrete-time data sequence;
j': time discrete data subscripts;
i: dimension;
Δ t': is the time interval between discrete points j '-1 and j';
acquiring the maximum value in the sequence y (j ', i), wherein the positive and negative inversion points are special points, and the number of the special points is determined by the value of delta t';
to fi(j') applying an iterative differentiation algorithm to the sequence data:
Δt1': is a discrete point j2+ k-1 and j2A time interval between + k;
k：Δt1the increment of 'from 1 to N'; n' is a constant;
j2: time of dayNumerical serial numbers of the points;
i: dimension;
for matrix d (j)2Sorting and adding the data points in the vectors according to the vectors in k), and selecting the maximum value and the sum of the data points in each vector as a slope and an amplitude;
aiming at the reconstruction function, the processed result vectors y (j', i) and d (j) are processed2K) generating mode types omega 1, omega 2, …, and omega c, and then calculating the distance of each wavelet reconstruction function by using the distance function between the modes; obtaining the variation, autocorrelation and cross-correlation values of the wavelet reconstruction function;
calculating a wavelet reconstruction function fiIntegration of (t):
Si= ∫fi（t）*dt
t: time;
i: dimension;
obtaining the area value of each function;
Ti(h) the vector expresses the waveform characteristics and rules of the wavelet reconstruction function; distributing eye movement electric signals and frontal muscle electric signals in collected brain waves in wavelet reconstruction functions of specific scales, and aiming at the wavelet reconstruction functions f'i(t), first, the first derivative is found:
Di（j2'）=（f'i（j2'）-f'i（j2'+Δt2））/Δt2
f'i（j2') is f'i(t) the discrete-time data sequence;
i: dimension;
j2': time discrete data subscripts;
Δt2: is a discrete point j2' -1 and j2The time interval of';
to Di（j2') sorting to obtain maximum and minimum values, setting threshold Q, and obtaining Di（j2') to obtain a set of extreme point vectors:
Mi(t 1): a high point;
mi(t 2): a low point;
i: dimension;
t1, t 2: the time point of the extreme value;
adopting an integral algorithm for wavelet reconstruction functions related to eye movement and myoelectricity:
SS =∫f'i（t）^2 * dt
obtaining the power values of myoelectricity and eye movement; aiming at the result data, through a normalization combination algorithm:
Sq=exp(a’*abs（Mi-mi）+b’*SS)；
a 'and b' are weighting coefficients which are constants and are determined by the signal expression range;
sq indicates quantitative expression of signal intensity of interference as one of display results;
wavelet reconstruction function with eye movement and myoelectricity eliminated from reconstruction function
And adopting a power spectrum algorithm:
t: a point in time;
i: dimension;
obtaining the components of the power spectrum in the reconstructed brain waves:
Fi={α，β，δ，θ，sef，mef}；
i: dimension;
α, β, δ, θ: power percentage of each band;
sef: edge frequencies of the power spectrum calculation;
mef: center frequency of power spectrum calculation;
combining the feature vectors obtained by waveform identification to form a group of feature data covering time domain and frequency domain:
Gi（j3）={Ti，Fi}；
i: dimension;
j3: a characteristic data sequence number;
data vector Gi（j3) A group, which is a primary processing result of the brain wave wavelet reconstruction function, is named as a metadata group of brain wave primary processing, and is used as basic data of secondary calculation;
extracting feature data in reconstruction function metadata of each scale, and calculating the ratio of the feature data corresponding to each scale, the slope, the change rate and the integral area of the feature data of each scale:
Yi2（j3）= { Gi（j3）-Gi（j3-1）/ Δj3 } ∈ z ；
Zi2（j3）={ Gi（j3）/ Gi（j3-1）} ∈ z ；
Vi2（j3）= {∫Gi（j3）*Δj3 } ∈ z ；
Li2（j3）= { abs（Gi（j31）-Gi（j32））} ∈ z ；
j3、j31 、j32: a characteristic data sequence number;
i 2: a result sequence number;
Δj3: increment corresponding to the characteristic data serial number;
z: a time domain computation space;
and for each data sequence, obtaining a series of data calculation formulas through data weighting:
Ei2={a1，b1，c1，d1}*{Yi2，Zi2，Vi2，Li2}；
i 2: a result sequence number;
a1、b1、c1、d1: weighting coefficient, constant;
to Ei2Data application normalization calculation:
pain index = (exp (E)0)）×100
Analgesic index = (exp (E)1)）×100
Tensity index = (exp (E)2)）×100
Delirium index = (exp (E)3)）×100
Anxiety index = (exp (E)4)）×100
Forgetting index = (exp (E)5)）×100
Comfort index = (exp (E)6)）×100；
The real-time objective quantitative characteristic indexes of pain, analgesia, anxiety, tension, delusions, forgetfulness and comfort in brain waves are obtained.
2. The method for extracting and expressing a pain component in brain waves according to claim 1, wherein: quantitative expressions of analgesia, anxiety, tension, delusions, forgetfulness and comfort are synchronously extracted from brain waves, and the quantitative expressions of pain depth are combined to complete the pain measurement process, so that the completeness and the accuracy of the pain measurement are achieved.
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