CN108968963A - A kind of medical treatment and nursing breathing detection equipment and its detection system - Google Patents
A kind of medical treatment and nursing breathing detection equipment and its detection system Download PDFInfo
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- CN108968963A CN108968963A CN201810668731.1A CN201810668731A CN108968963A CN 108968963 A CN108968963 A CN 108968963A CN 201810668731 A CN201810668731 A CN 201810668731A CN 108968963 A CN108968963 A CN 108968963A
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- respiratory rate
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- nursing
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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
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
- A61B5/0816—Measuring devices for examining respiratory frequency
-
- 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
- A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
Abstract
The invention belongs to the field of medical instrument technology, a kind of medical treatment and nursing breathing detection equipment and its detection system are disclosed；It is provided with main control module, fixed module, monitoring modular, alarm module, display module.The system can respiratory rate to patient, depth, the rhythm and pace of moving things and breathing whether be normally carried out monitoring, increase working efficiency, reduce man power and material.The present invention extracts breath signal from electrocardio and pulse wave, calculates respiratory rate using AR model power Power estimation algorithm, and estimate respiratory rate by the multichannel data blending algorithm based on Kalman filtering and Signal quality assessment.Data anastomosing algorithm preferably reflects the variation of respiratory rate, with pressure resistance type respiration transducer provide reference respiratory rate compared with, error for (- 0.03 ± 2.78) it is secondary/min.Method based on multichannel data fusion can accurately estimate respiratory rate, lay a good foundation for clinical monitoring of respiration and the application in sleep apnea research.
Description
Technical field
The invention belongs to the field of medical instrument technology more particularly to a kind of medical treatment and nursing breathing detection equipment and its detections
System.
Background technique
Currently, breathing is very important physiological signal, can differentiating patient by respiration, whether there are also physiology
Feature, while can differentiate whether the physiological characteristic of patient is normal by the respiratory intensity and respiratory rate of analysis patient.Mesh
The preceding breathing detection equipment and system for needing a kind of nursing, it is whether normal for detecting patient physiological characteristic, it is reduced with this
The working strength of health care workers improves working efficiency.
In conclusion problem of the existing technology is:
(1) the breathing detection system of nursing at present uses manual operation, and labor intensity is big, and working efficiency is low.
(2) electric signal is carried out modeling operation time using the powerful Gaussian process of capability of fitting, and prediction error is big.
(3) the reference respiratory rate error of pressure resistance type respiration transducer is larger, cannot accurately estimate respiratory rate.
Summary of the invention
In view of the problems of the existing technology, the present invention provides a kind of medical treatment and nursing breathing detection equipment and its detection
System.
The invention is realized in this way a kind of medical treatment and nursing breathing detection method, the medical treatment and nursing breathing detection
Method includes:
The electric signal of outflow, electric signal is reinforced, and is transmitted to alarm module and display module；
The electric signal y (t) are as follows:
Y (t)=c (t) m (t)；
In formula, c (t) is adjustment signal, and m (t) is Gaussian noise；
For arbitrary finite stochastic variable x1..., xn, wherein n >=1, and be arbitrary integer, corresponding process status
f(x1), f (x2) ..., f (xn) joint probability distribution obey n tie up Gaussian Profile, detailed process can be by mean value m (x) and covariance
Function k (x, x ') is determined:
F (x)~GP (m (x), k (x, x '))；
Given training D={ xi, yi) | i=1,2 ..., N }, wherein xi∈RdFor input quantity, yi∈ R is output vector, then
Output vector y forms y~N by the Gaussian prior distribution of zero-mean function m (x) and the covariance function k (x, x ') of a positive definite
(m, p)；Input new vector x*∈Rd, KNFor the covariance matrix of training sample, then the Gaussian Profile of corresponding data to be predicted
Function is y~N (m*, p*):
By multiresolution discrete wavelet transformer change commanders electric signal y (t) decompose:
For scaling function, ψ (t) is wavelet function, the expression formula of scale coefficient and wavelet coefficient are as follows:
dj(k)=< y (t), ψJ, k(t) >=| y | (t) ψJ, k(t)dt；
The corresponding gauss of distribution function of electric signal are as follows:
cj~N (mc, pc), dj~N (md, pd)；
Equipment is fixed on the face of patient；
Respiratory rate and respiratory intensity to patient carry out real-time monitoring；
State value x (the x ∈ R of the respiratory raten) and predicted value z (z ∈ Rn) decibel meets discrete time process difference side
Journey:
xk=Axk-1+Buk+wk-1
zk=Hxk+vk；
In formula, procedure activation noise w and measurement noise v be mutually indepedent, normal distribution mean value be 0, covariance difference
For the white noise of Q and R；Matrix A, B and H are state transformation coefficient matrix, and u is optional control input variable；Kalman filtering is passed
It pushes away are as follows:
In formula,In the state of for before known kth step, the respiratory rate prior state estimation of kth step,To provide
The respiratory rate estimated value z of kth stepkThe posteriority state estimation of respiratory rate afterwards；For the covariance of prior estimate error, PkIt is rear
Test estimation error covariance；Q is the covariance of procedure activation noise, and R is the covariance for measuring noise,
For the residual error of measurement process, KkFor the gain coefficient of residual error；
R=R0exp(1/SQI2-1)；
In formula, SQI is signal quality index；
When the respiratory rate of patient is abnormal, alarm is proposed by primary dcreening operation model in time；
To the respiratory rate of patient, breathes strong and weak information and shown.
Further, the primary dcreening operation model that passes through: y (n)=x (n)-x (n-1), the electrocardiogram (ECG) data being currently received are x (n),
And be the latter sampled value of S wave crest, x (m) is R crest value, and x (i) is a sampled value after Q wavelength-division value, and is met:
The identification of S wave:
Y (n) >=0, y (n-1)≤- 1, y (n-2) |≤- 1；
The identification of R wave:
Y (m+3)≤- 1, y (m+2)≤- 3, y (m+1) >=1；
The identification of Q wave:
Y (i) >=0, y (i-1)≤1, y (i-2)≤1.
Another object of the present invention is to provide a kind of medical treatment and nursing for realizing the medical treatment and nursing breathing detection method
With breathing detection system, the medical treatment and nursing breathing detection detection system includes:
Electric signal is reinforced for will test the electric signal of module outflow, is transmitted to alarm module and display by main control module
Module；
Fixed module is connected with main control module, for the equipment to be fixed on to the face of patient；
Monitoring modular is connected with main control module, for the respiratory rate and depth of respiration, rhythm and pace of moving things progress reality to patient
When monitoring；
Alarm module is connected with main control module, when abnormal for the respiratory rate as patient, timely proposes
Alarm；
Display module is connected with main control module, for the respiratory rate to patient, breathes the information such as power and is shown
Show.
Further, the fixed module is mask, and mask is provided with rubber rope, mask is buckled in patient facial region, passes through rubber
Glue rope ties up to hindbrain position.
Further, the monitoring modular is the air flow sensor being pasted on fixed module, by patient respiratory in air-flow
The size of inductor overdraught.
Further, the alarm module is that buzzer passes through air-flow sense after air flow sensor does not experience air-flow pressure
Answer the electric current of device almost nil, alarm takes place from other circuit reception electric current in the buzzer of alarm module at this time.
Further, the display module is display screen, for the respiratory rate to patient, breathes strong and weak information and is shown
Show.
Advantages of the present invention and good effect are as follows: the medical treatment and nursing breathing detection equipment and its detection system.To telecommunications
Number carry out the prediction of Gaussian process regression modeling, it is non-linear that its can be fitted well；When Gaussian process models, simulation results show,
Gaussian process has good capability of fitting and powerful Generalization Capability to electric signal；Using the Gauss modeling based on small echo, solve
It is simple to model bring long operational time and the big disadvantage of non-identity set prediction error using Gaussian process；From simulation result
From the point of view of, the prediction effect after introducing small echo is reducing error and is having apparent advantage on operation time.The present invention from electrocardio and
Breath signal is extracted in pulse wave, calculates respiratory rate using AR model power Power estimation algorithm, and by being based on Kalman filtering
Respiratory rate is estimated with the multichannel data blending algorithm of Signal quality assessment.Data anastomosing algorithm preferably reflects the change of respiratory rate
Change, with pressure resistance type respiration transducer provide reference respiratory rate compared with, error for (- 0.03 ± 2.78) it is secondary/min.Based on multichannel
The method of data fusion can accurately estimate respiratory rate, for clinical monitoring of respiration and answering in sleep apnea research
With laying a good foundation.
Detailed description of the invention
Fig. 1 is the structural schematic diagram of the detection system of medical treatment and nursing breathing detection equipment provided in an embodiment of the present invention；
In figure: 1, main control module；2, fixed module；3, monitoring modular；4, alarm module；5, display module.
Specific embodiment
In order to further understand the content, features and effects of the present invention, the following examples are hereby given, and cooperate attached drawing
Detailed description are as follows.
Structure of the invention is explained in detail with reference to the accompanying drawing.
As shown in Figure 1, medical treatment and nursing breathing detection system provided in an embodiment of the present invention includes: main control module 1, fixes
Module 2, monitoring modular 3, alarm module 4, display module 5.
Electric signal is reinforced for will test the electric signal of module outflow, is transmitted to alarm module 4 and shows by main control module 1
Show module 5.
Fixed module 2 is connected with main control module 1, for the equipment to be fixed on to the face of patient.
Monitoring modular 3 is connected with main control module 1, for patient respiratory rate and respiratory intensity carry out it is real-time
Monitoring.
Alarm module 4 is connected with main control module 1, when abnormal for the respiratory rate as patient, timely mentions
Alarm out.
Display module 5 is connected with main control module 1, for the respiratory rate to patient, breathes the information such as power and is shown
Show.
Fixed module of the invention is a mask, and mask is provided with rubber rope, mask is buckled in patient facial region surface, is passed through
Rubber rope ties up to hindbrain position, so that the device is able to be fixed on the face of patient.Alarm module 4 is buzzer, when air-flow sense
Almost nil by the electric current of air flow sensor after answering device not experience air-flow pressure, the buzzer of alarm module is from another at this time
External circuit receives electric current and alarm takes place.Detection module 3 is the air flow sensor being pasted on fixed module, is exhaled by patient
The size in air flow sensor overdraught is inhaled, certain air-flow pressure is generated to inductor, so that the sheet resistance of air flow sensor
It changes, so that generate variation by the electric current of air flow sensor, with this come whether normal, the respiratory rate that incudes patient respiratory
Whether normal and respiratory intensity how.Display module 5 is display screen, for the respiratory rate to patient, breathes the letter such as power
Breath is shown.Main control module 1 will test the electric signal of the outflow of module 3 as signal terminal, after electric signal is reinforced, transmit
To alarm module 4 and display module 5.
Application principle of the invention is further described combined with specific embodiments below.
Medical treatment and nursing breathing detection method provided in an embodiment of the present invention includes:
The electric signal of outflow, electric signal is reinforced, and is transmitted to alarm module and display module；
The electric signal y (t) are as follows:
Y (t)=c (t) m (t)；
In formula, c (t) is adjustment signal, and m (t) is Gaussian noise；
For arbitrary finite stochastic variable x1..., xn, wherein n >=1, and be arbitrary integer, corresponding process status
f(x1), f (x2) ..., f (xn) joint probability distribution obey n tie up Gaussian Profile, detailed process can be by mean value m (x) and covariance
Function k (x, x ') is determined:
F (x)~GP (m (x), k (x, x '))；
Given training D={ (xi, yi) | i=1,2 ..., N }, wherein xi∈RdFor input quantity, yi∈ R is output vector, then
Output vector y forms y~N by the Gaussian prior distribution of zero-mean function m (x) and the covariance function k (x, x ') of a positive definite
(m, p)；Input new vector x*∈Rd, KNFor the covariance matrix of training sample, then the Gaussian Profile of corresponding data to be predicted
Function is y~N (m*, p*):
By multiresolution discrete wavelet transformer change commanders electric signal y (t) decompose:
For scaling function, ψ (t) is wavelet function, the expression formula of scale coefficient and wavelet coefficient are as follows:
dj(k)=< y (t), ψJ, k(t) >=∫ y | (t) ψJ, k(t)dt；
The corresponding gauss of distribution function of electric signal are as follows:
cj~N (mc, pc), dj~N (md, pd)；
Equipment is fixed on the face of patient；
Respiratory rate and respiratory intensity to patient carry out real-time monitoring；
State value x (the x ∈ R of the respiratory raten) and predicted value z (z ∈ Rn) decibel meets discrete time process difference side
Journey:
xk=Axk-1+Buk+wk-1
zk=Hxk+vk；
In formula, procedure activation noise w and measurement noise v be mutually indepedent, normal distribution mean value be 0, covariance difference
For the white noise of Q and R；Matrix A, B and H are state transformation coefficient matrix, and u is optional control input variable；Kalman filtering is passed
It pushes away are as follows:
In formula,In the state of for before known kth step, the respiratory rate prior state estimation of kth step,To provide
The respiratory rate estimated value z of kth stepkThe posteriority state estimation of respiratory rate afterwards；For the covariance of prior estimate error, PkIt is rear
Test estimation error covariance；Q is the covariance of procedure activation noise, and R is the covariance for measuring noise,
For the residual error of measurement process, KkFor the gain coefficient of residual error；
R=R0exp(1/SQI2-1)；
In formula, SQI is signal quality index；
When the respiratory rate of patient is abnormal, alarm is proposed by primary dcreening operation model in time；
To the respiratory rate of patient, breathes strong and weak information and shown.
Further, the primary dcreening operation model that passes through: y (n)=x (n)-x (n-1), the electrocardiogram (ECG) data being currently received are x (n),
And be the latter sampled value of S wave crest, x (m) is R crest value, and x (i) is a sampled value after Q wavelength-division value, and is met:
The identification of S wave:
Y (n) >=0, y (n-1)≤- 1, y (n-2) |≤- 1；
The identification of R wave:
Y (m+3)≤- 1, y (m+2)≤- 3, y (m+1) >=1；
The identification of Q wave:
Y (i) >=0, y (i-1)≤1, y (i-2)≤1.
The above is only the preferred embodiments of the present invention, and is not intended to limit the present invention in any form,
Any simple modification made to the above embodiment according to the technical essence of the invention, equivalent variations and modification, belong to
In the range of technical solution of the present invention.
Claims (7)
1. a kind of medical treatment and nursing breathing detection method, which is characterized in that the medical treatment and nursing breathing detection method includes:
The electric signal of outflow, electric signal is reinforced, and is transmitted to alarm module and display module；
The electric signal y (t) are as follows:
Y (l)=c (t) m (l)；
In formula, c (t) is adjustment signal, and m (t) is Gaussian noise；
For arbitrary finite stochastic variable x1..., xn, wherein n >=1, and be arbitrary integer, corresponding process status f
(x1), f (x2) ..., f (xn) joint probability distribution obey n tie up Gaussian Profile, detailed process can be by mean value m (x) and covariance
FunctionIt determines:
Given training D={ (xi, yi) | i=1,2 ..., N }, wherein xi∈RdFor input quantity, yi∈ R is output vector, then exports
Vector y by zero-mean function m (x) and a positive definite covariance functionGaussian prior distribution composition y~N (m,
p)；Input new vector x*∈Rd, KNFor the covariance matrix of training sample, then the Gaussian Profile letter of corresponding data to be predicted
Number is y~N (m*, p*):
By multiresolution discrete wavelet transformer change commanders electric signal y (t) decompose:
For scaling function, φ (t) is wavelet function, the expression formula of scale coefficient and wavelet coefficient are as follows:
The corresponding gauss of distribution function of electric signal are as follows:
cj~N (mc, pc), dj~N (md, pd)；
Equipment is fixed on the face of patient；
Respiratory rate and respiratory intensity to patient carry out real-time monitoring；
State value x (the x ∈ R of the respiratory raten) and predicted value z (z ∈ Rn) decibel meets discrete time process difference equation:
In formula, procedure activation noise w and measurement noise v be mutually indepedent, normal distribution mean value be 0, covariance be respectively Q and
The white noise of R；Matrix A, B and H are state transformation coefficient matrix, and u is optional control input variable；Kalman filtering recursion are as follows:
In formula,In the state of for before known kth step, the respiratory rate prior state estimation of kth step,To provide kth step
Respiratory rate estimated valueThe posteriority state estimation of respiratory rate afterwards；For the covariance of prior estimate error,Estimate for posteriority
Count the covariance of error；Q is the covariance of procedure activation noise, and R is the covariance for measuring noise,To survey
The residual error of amount process,For the gain coefficient of residual error；
R=R0exp(1/SQI2-1)；
In formula, SQI is signal quality index；
When the respiratory rate of patient is abnormal, alarm is proposed by primary dcreening operation model in time；
To the respiratory rate of patient, breathes strong and weak information and shown.
2. medical treatment and nursing as described in claim 1 breathing detection method, which is characterized in that described to pass through primary dcreening operation model: y
(n)=x (n)-x (n-1), the electrocardiogram (ECG) data being currently received are x (n), and are the latter sampled value of S wave crest, and x (m) is R wave
Peak value, x (i) is a sampled value after Q wavelength-division value, and is met:
The identification of S wave:
Y (n) >=0, y (n-1)≤- 1, y (n-2) |≤- 1；
The identification of R wave:
Y (m+3)≤- 1, y (m+2)≤- 3, y (m+1) >=1；
The identification of Q wave:
Y (i) >=0, y (i-1)≤1, y (i-2)≤1.
3. a kind of medical treatment and nursing breathing detection system for realizing medical treatment and nursing breathing detection method described in claim 1,
It is characterized in that, the medical treatment and nursing breathing detection detection system includes:
Electric signal is reinforced for will test the electric signal of module outflow, is transmitted to alarm module and display mould by main control module
Block；
Fixed module is connected with main control module, for the equipment to be fixed on to the face of patient；
Monitoring modular is connected with main control module, for patient respiratory rate and depth of respiration, the rhythm and pace of moving things carry out it is real-time
Monitoring；
Alarm module is connected with main control module, when abnormal for the respiratory rate as patient, timely proposes police
Report；
Display module is connected with main control module, for the respiratory rate to patient, breathes the information such as power and is shown.
4. medical treatment and nursing as claimed in claim 3 breathing detection system, which is characterized in that the fixed module is mask,
Mask is provided with rubber rope, and mask is buckled in patient facial region, ties up to hindbrain position by rubber rope.
5. medical treatment and nursing as claimed in claim 3 breathing detection system, which is characterized in that the monitoring modular is to be pasted onto
Air flow sensor on fixed module, by patient respiratory air flow sensor overdraught size.
6. medical treatment and nursing as claimed in claim 3 breathing detection system, which is characterized in that the alarm module is buzzing
Device, after air flow sensor does not experience air-flow pressure, almost nil by the electric current of air flow sensor, alarm module at this time
Buzzer receives electric current from other circuit and alarm takes place.
7. medical treatment and nursing as claimed in claim 3 breathing detection system, which is characterized in that the display module is display
Screen breathes strong and weak information and is shown for the respiratory rate to patient.
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| CN201810668731.1A CN108968963A (en) | 2018-06-26 | 2018-06-26 | A kind of medical treatment and nursing breathing detection equipment and its detection system |
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| CN201810668731.1A CN108968963A (en) | 2018-06-26 | 2018-06-26 | A kind of medical treatment and nursing breathing detection equipment and its detection system |
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| CN112932460A (en) * | 2021-02-01 | 2021-06-11 | 重庆大学 | Respiratory rate monitoring device and method |
| CN116077050A (en) * | 2023-01-30 | 2023-05-09 | 青岛海尔科技有限公司 | Respiratory diagnosis method, respiratory diagnosis device, storage medium and electronic device |
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Application publication date: 20181211 |