CN110786855A - Sputum induction device and control method thereof - Google Patents
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Abstract
The sputum induction apparatus provided by the invention comprises: the sensing detection device is attached to the chest of the patient, is positioned at the connecting position of the trachea and the lung, and can detect and obtain the respiratory sound wave of the patient; the pressure sensor is arranged at the mouth of the artificial trachea and can detect the respiratory pressure of a patient; the data conversion module is connected with the sensor detection device and can convert the analog signals detected by the sensor detection device into digital signals; the analysis processing module is connected with the data conversion module and can calculate the sputum indication information of the patient through the digital signal; the control module is electrically connected with the analysis processing module, comprises a display storage unit and an instruction unit, is used for displaying and storing image information and sputum indication information and sending instructions, and also provides a control method of the sputum induction device.
Description
Technical Field
The invention relates to the field of medical instruments, in particular to a sputum induction device and a control method of the sputum induction device.
Background
When a patient with an artificial airway is nursed, the patient is observed whether to inhale sputum, the waveform of a breathing machine is usually needed to be seen, or the lung sputum buzzing sound of the patient is stopped to be diagnosed, or the choking cough reflex of the patient is observed, so that the condition of the patient is not observed timely, the workload of medical staff is increased, and the infection incidence rate of the patient is increased. Therefore, a sputum sensor is urgently needed, sputum of the lung of a patient can be visually detected, and timely and effective cleaning is carried out.
The invention application with the application number of 201910088164.7 discloses a sputum intelligent sensing device, which is placed at an artificial airway opening to detect sputum indication information of a patient, but is only suitable for an artificial airway patient and is not suitable for a patient who is in bed and can not independently expectorate.
The invention document with the application number of 201510884295.8 discloses a non-contact respiration detection device, which has a light emitting module, irradiates light waves on the chest of a patient, and receives the light waves by a light receiving module to obtain respiration image information.
Disclosure of Invention
The invention designs and develops a sputum induction device, which is applied to the chest of a patient, obtains sputum indication information of the patient by detecting respiratory sound waves of the patient, is suitable for various patients and avoids the patient from being choked.
The invention also provides a control method of the sputum induction device, which can predict the respiratory sound wave of the next time period according to the respiratory sound wave detected in the current time period, and input the respiratory sound wave of the next time period into the neural network controller to obtain the sputum indication information of the next time period, thereby realizing automatic detection and high accuracy.
The technical scheme provided by the invention is as follows:
a sputum induction apparatus comprising:
the sensing detection device is attached to the chest of the patient, is positioned at the connecting position of the trachea and the lung, and can detect and obtain the respiratory sound wave of the patient;
the pressure sensor is arranged in the artificial trachea opening or the breathing mask and can detect the breathing pressure of the patient;
the data conversion module is connected with the sensor detection device and can convert the analog signals detected by the sensor detection device into digital signals;
the analysis processing module is connected with the data conversion module and can calculate the sputum indication information of the patient through the digital signal;
and the control module is electrically connected with the analysis processing module, comprises a display storage unit and an instruction unit, and is used for displaying and storing the image information and the sputum indication information and sending out an instruction.
Preferably, the sensing device is applied to the patient's chest by an adhesive patch.
Preferably, the sputum induction apparatus according to claim 2, wherein the acoustic wave sensor is a silent capacitance sensor.
A sputum induction apparatus control method includes:
step one, detecting and reading sound wave vibration data detected by a sound wave sensor in real time and pressure data detected by a pressure sensor;
step two, calculating (t) according to the vibration datai-t0)～tiA group of measured vectors relating to breathing index over a period of time;
wherein the actually measured vector group of the respiration index comprises an actually measured oscillation frequency coefficient ηrAnd actually measuring the maximum oscillation intensity Ir，tiIs the current time, t0Is a time interval;
step three, according to the formula (t)i-t0)～tiPredicting t with respect to a group of measured vectors of a breathing index over a time periodi～(ti+t0) A group of prediction vectors for a segment with respect to a respiration index;
wherein the prediction vector group of the respiration index comprises a prediction oscillation frequency ηr+1And predicting the concussion intensity Ir+1；
Step four, according to (t)i-t0)～tiDetected pressure data P over a period of timeλPredicting the next time period t of the patienti～(ti+t0) Pressure data P ofλ+1；
Inputting the data including the predicted concussion frequency, the predicted concussion intensity and the predicted pressure into a neural network controller to obtain a vector group representing the sputum indication; and
and outputting the vector group representing the sputum indication as a sputum induction result.
Preferably, the oscillation frequency calculation formula is as follows:
wherein, ηr+1For predicting the oscillation frequency, λ is an average coefficient related to the oscillation frequency, ηrTo actually measure the oscillation frequency, Ir+1In order to predict the intensity of the oscillations,
is the average oscillation frequency, a1Is the first oscillation coefficient, kλ+1To predict the intensity rise coefficient, kλIs the actual intensity rise coefficient;
preferably, the predicted oscillation intensity calculation formula is as follows:
wherein, ηsThe oscillation frequency is predicted for the average.
Preferably, the predicted pressure data calculation formula is:
wherein the content of the first and second substances,is the average pressure value.
Preferably, the calculation process of the neural network controller is as follows:
step 1, predicting oscillation frequency η of parameters in sequencer+1And predicting the concussion intensity Ir+1Normalizing with the predicted pressure data;
step 2, determining an input layer neuron vector x ═ x of the three-layer BP neural network1,x2,x3In which x1To predict the oscillation frequency coefficient, x2To predict the oscillation intensity coefficient, x3Is a predicted pressure data index;
and 3, mapping the input layer vector to a hidden layer, wherein the hidden layer vector y is { y ═ y1,y2…ymM is the number of hidden nodes;
step 4, obtaining neuron vectors of an output layero＝{o1,o2,o3}; wherein o is1Is the coefficient of sputum volume, o2Is the coefficient of consistency of sputum o3Is the sputum obstruction coefficient;
step 5, the controller outputs a control strategy relational expression of the next period;
wherein the output layer o3Has a neuron value of 0 or 1 when o3When the number is 1, the patient needs to be sputum-aspirated at this time, when the number is o3When 0, no treatment is performed.
Preferably, the number m of hidden nodes satisfies:
wherein n is the number of nodes of the input layer, and p is the number of nodes of the output layer.
Preferably, the oscillation frequency η is predictedr+1Predicting the oscillation intensity Ir+1And predicted pressure data Pλ+1The normalization formula of (a) is:
wherein x isjAs a parameter in the input layer vector, xjJ is 1,2,3 is the parameter η respectivelyr+1、Ir+1、Pλ+1；xjmaxAnd xjminRespectively, a maximum value and a minimum value in the corresponding measured parameter.
The invention has the advantages of
The invention designs and develops a sputum induction device, which is applied to the chest of a patient, obtains sputum indication information of the patient by detecting respiratory sound waves of the patient, is suitable for various patients and avoids the patient from being choked.
The invention also provides a control method of the sputum induction device, which can predict the respiratory sound wave of the next time period according to the respiratory sound wave detected in the current time period, and input the respiratory sound wave of the next time period into the neural network controller to obtain the sputum indication information of the next time period, thereby realizing automatic detection and high accuracy.
Drawings
FIG. 1 is a schematic structural diagram of the sputum induction apparatus according to the present invention.
FIG. 2 is a flow chart of a method for controlling the sputum-sensing device according to the present invention.
Detailed Description
The present invention is further described in detail below to enable those skilled in the art to practice the invention with reference to the description
As shown in fig. 1, the sputum induction apparatus provided by the present invention comprises: a sensing detection device 210, a pressure sensor 220, a data conversion module 230, an analysis processing module 240, and a control module 250.
The sensing detection device 210 is attached to the chest of the patient and located at the connecting position of the trachea and the lung, and can detect and obtain the respiratory sound wave of the patient; the pressure sensor 220 is arranged in the artificial trachea opening or the breathing mask and can detect the breathing pressure of the patient; the data conversion module 230 is connected to the sensor detection device 210, and can convert the analog signal detected by the sensor detection device 210 into a digital signal; the analysis processing module 240 is connected with the data conversion module 230, and can calculate the sputum indication information of the patient by the digital signal; the control module 250 is electrically connected to the analysis processing module 240, and includes a display storage unit and an instruction unit, and is configured to display and store the image information and the sputum indication information, and issue an instruction. The sensing device 210 is applied to the patient's chest by an adhesive patch, preferably a silent capacitive sensor.
As shown in fig. 2, the method for controlling a sputum sensor according to the present invention comprises:
step 110, detecting and reading sound wave vibration data detected by a sound wave sensor in real time and pressure data detected by a pressure sensor;
step 120, obtaining (t) according to the detected sound wave vibration datai-t0)～tiA group of measured vectors related to the breathing index within the time interval, wherein the group of measured vectors of the breathing index comprises a measured oscillation frequency coefficient ηrAnd actually measuring the maximum oscillation intensity Ir，tiIs at presentTime of day t0Is a time interval;
step 130, according to the formula (t)i-t0)～tiPredicting t with respect to a group of measured vectors of a breathing index over a time periodi～(ti+t0) A group of prediction vectors for a segment with respect to a respiration index;
wherein the prediction vector group of the respiration index comprises η prediction oscillation frequenciesr+1And predicting the concussion intensity Ir+1；
The calculation formula of the oscillation frequency is as follows:
wherein, ηr+1For predicting the oscillation frequency, λ is an average coefficient related to the oscillation frequency, ηrTo actually measure the oscillation frequency, Ir+1In order to predict the intensity of the oscillations,
is the average oscillation frequency, a1Is the first oscillation coefficient, kλ+1To predict the intensity rise coefficient, kλIs the actual intensity rise coefficient;
the calculation formula of the predicted oscillation intensity is as follows:
wherein, ηsThe oscillation frequency is predicted for the average.
Step 140, according to (t)i-t0)～tiDetected pressure data P over a period of timeλPredicting the next time period t of the patienti～(ti+t0) Pressure data P ofλ+1(ii) a The predicted pressure data is calculated by the formula:
wherein the content of the first and second substances,
is the average pressure value.
Step 150, inputting the data including the predicted concussion frequency, the predicted concussion intensity and the predicted pressure into a neural network controller to obtain a vector group representing the sputum indication; and
and outputting the vector group representing the sputum indication as a sputum induction result.
And establishing a BP neural network model.
Fully interconnected connections are formed among neurons of each layer on the BP model, the neurons in each layer are not connected, and the output and the input of neurons in an input layer are the same, namely oi＝xi. The operating characteristics of the neurons of the intermediate hidden and output layers are:
opj＝fj(netpj)
where p represents the current input sample, ωjiIs the connection weight from neuron i to neuron j, opiIs the current input of neuron j, opjIs the output thereof; f. ofjIs a non-linear, slightly non-decreasing function, generally taken as a sigmoid function, i.e. fj(x)＝1/(1+e-x)。
The BP network system structure adopted by the invention consists of three layers, wherein the first layer is an input layer, n nodes are provided in total, n detection signals representing the working state of the equipment are correspondingly provided, and the signal parameters are given by a data preprocessing module; the second layer is a hidden layer with m nodes,
wherein m is the number of intermediate layer nodes, n is the number of input layer nodes, and p is the number of output layer nodes; the third layer is an output layer, p nodes are provided in total, and the output is determined by the response actually needed by the system.
The mathematical model of the network is:
inputting a layer vector: x ═ x1,x2,…,xn)T
Intermediate layer vector: y ═ y1,y2,…,ym)T
Outputting a layer vector: z is (z)1,z2,…,zp)T
According to the sampling period, the number of nodes of an input layer is n-3, the number of nodes of an output layer is p-3, and the number of nodes of a hidden layer is estimated according to the following formula:
the data acquired by the sensors belong to different physical quantities, and the dimensions of the data are different. Therefore, the data needs to be normalized to a number between 0-1 before it is input into the artificial neural network.
Normalized to predict oscillation frequency ηr+1Predicting the oscillation intensity Ir+1And predicted pressure data Pλ+1The normalization formula of (a) is:
wherein x isjAs a parameter in the input layer vector, xjJ is 1,2,3 is the parameter η respectivelyr+1、Ir+1、Pλ+1；xjmaxAnd xjminAnd respectively adopting S-shaped functions for the maximum value and the minimum value in the corresponding measurement parameters.
Specifically, η for predicting oscillation frequencyr+1Normalized to obtain the predicted oscillation frequency coefficient x1：
Therein, max (η)r+1) And min (η)r+1) Respectively, the maximum value and the minimum value of the predicted oscillation frequency.
Likewise, the concussion intensity I is predictedr+1Normalization is performed by the following equation to obtain a predictionCoefficient of oscillation intensity x2：
Wherein, max (I)r+1) And max (I)r+1) The maximum value and the minimum value of the predicted oscillation intensity are respectively.
Normalizing the predicted pressure data to obtain a predicted pressure data coefficient x3：
Wherein, max (P)λ+1) And min (P)λ+1) The maximum and minimum values of the predicted pressure data coefficient, respectively.
Obtaining output layer neuron vector o ═ o1,o2,o3}; wherein o is1Is the coefficient of sputum volume, o2Is the coefficient of consistency of sputum o3Is the sputum obstruction coefficient; .
And (5) training the BP neural network.
After the BP neural network node model is established, the training of the BP neural network can be carried out. Obtaining training samples according to empirical data of the product, and giving a connection weight w between an input node i and a hidden layer node jijConnection weight w between hidden layer node j and output layer node kjkThreshold value theta of hidden layer node jjThreshold value w of node k of output layerij、wjk、θj、θkAre all random numbers between-1 and 1.
(1) Training method
Each subnet adopts a separate training method; when training, firstly providing a group of training samples, wherein each sample consists of an input sample and an ideal output pair, and when all actual outputs of the network are consistent with the ideal outputs of the network, the training is finished; otherwise, the ideal output of the network is consistent with the actual output by correcting the weight; the output samples for each subnet training are shown in table 1.
TABLE 1 output samples for network training
(2) Training algorithm
The BP network is trained by using a back Propagation (Backward Propagation) algorithm, and the steps can be summarized as follows:
the first step is as follows: and selecting a network with a reasonable structure, and setting initial values of all node thresholds and connection weights.
The second step is that: for each input sample, the following calculations are made:
(a) forward calculation: for j unit of l layer
In the formula (I), the compound is shown in the specification,
for the weighted sum of the j unit information of the l layer at the nth calculation,is the connection weight between the j cell of the l layer and the cell i of the previous layer (i.e. the l-1 layer),is the previous layer (i.e. l-1 layer, node number n)l-1) The operating signal sent by the unit i; when i is 0, order
Is the threshold of the j cell of the l layer.
If the activation function of the unit j is a sigmoid function, then
And is
If neuron j belongs to the first hidden layer (l ═ 1), then there are
If neuron j belongs to the output layer (L ═ L), then there are
(b) And (3) calculating the error reversely:
for output unit
Pair hidden unit
(c) Correcting the weight value:
The third step: inputting a new sample or a new period sample until the network converges, and randomly re-ordering the input sequence of the samples in each period during training.
The BP algorithm adopts a gradient descent method to solve the extreme value of a nonlinear function, and has the problems of local minimum, low convergence speed and the like. A more effective algorithm is a Levenberg-Marquardt optimization algorithm, which enables the network learning time to be shorter and can effectively inhibit the network from being locally minimum. The weight adjustment rate is selected as
Δω＝(JTJ+μI)-1JTe
Wherein J is a Jacobian (Jacobian) matrix of error to weight differentiation, I is an input vector, e is an error vector, and the variable mu is a scalar quantity which is self-adaptive and adjusted and is used for determining whether the learning is finished according to a Newton method or a gradient method.
When the system is designed, the system model is a network which is only initialized, the weight needs to be learned and adjusted according to data samples obtained in the using process, and therefore the self-learning function of the system is designed. Under the condition of appointing learning samples and quantity, the system can carry out self-learning so as to continuously improve the network performance.
The sputum indication information relation is as follows:
wherein, ai+1Is ti～(ti+t0) Amount of sputum from time-phased patient, βi+1Is ti～(ti+t0) Time course patient's sputum consistency, Pi+1Is ti～(ti+t0) Time interval sputum obstruction coefficient. Wherein the output layer o3Has a neuron value of 0 or 1 when o3When the number is 1, the patient needs to be sputum-aspirated at this time, when the number is o3When 0, no treatment is performed.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.
Claims (10)
1. A sputum induction apparatus, comprising:
the sensing detection device is attached to the chest of the patient, is positioned at the connecting position of the trachea and the lung, and can detect and obtain the respiratory sound wave of the patient;
the pressure sensor is arranged in the artificial trachea opening or the breathing mask and can detect the breathing pressure of the patient;
the data conversion module is connected with the sensor detection device and can convert the analog signals detected by the sensor detection device into digital signals;
the analysis processing module is connected with the data conversion module and can calculate the sputum indication information of the patient through the digital signal;
and the control module is electrically connected with the analysis processing module, comprises a display storage unit and an instruction unit, and is used for displaying and storing the image information and the sputum indication information and sending out an instruction.
2. The sputum induction apparatus of claim 1, wherein the sensing device is applied to the patient's chest by an adhesive patch.
3. The sputum induction apparatus of claim 2, wherein the acoustic sensor is a silent capacitive sensor.
4. A method for controlling a sputum induction apparatus using the sputum induction apparatus according to any one of claims 1 to 3, comprising:
step one, detecting and reading sound wave vibration data detected by a sound wave sensor in real time and pressure data detected by a pressure sensor;
step two, calculating (t) according to the vibration datai-t0)～tiA group of measured vectors relating to breathing index over a period of time;
wherein the actually measured vector group of the respiration index comprises an actually measured oscillation frequency coefficient ηrAnd actually measuring the maximum oscillation intensity Ir，tiIs the current time, t0Is a time interval;
step three, according to the formula (t)i-t0)～tiPredicting t with respect to a group of measured vectors of a breathing index over a time periodi～(ti+t0) A group of prediction vectors for a segment with respect to a respiration index;
wherein the prediction vector group of the respiration index comprises a prediction oscillation frequency ηr+1And predicting the concussion intensity Ir+1；
Step four, according to (t)i-t0)～tiDetected pressure data P over a period of timeλPredicting the next time period t of the patienti～(ti+t0) Pressure data P ofλ+1；
Inputting the data including the predicted concussion frequency, the predicted concussion intensity and the predicted pressure into a neural network controller to obtain a vector group representing the sputum indication; and
and outputting the vector group representing the sputum indication as a sputum induction result.
5. The method of claim 4, wherein the oscillation frequency is calculated by the following formula:
wherein, ηr+1For predicting the oscillation frequency, λ is an average coefficient related to the oscillation frequency, ηrTo actually measure the oscillation frequency, Ir+1In order to predict the intensity of the oscillations,
is the average oscillation frequency, a1Is the first oscillation coefficient, kλ+1To predict the intensity rise coefficient, kλIs the actual intensity rise factor.
6. The method of claim 5, wherein the predicted concussion intensity is calculated by the following formula:
wherein, ηsThe oscillation frequency is predicted for the average.
7. The method of claim 6, wherein the predicted pressure data is calculated by the formula:
wherein the content of the first and second substances,
is the average pressure value.
8. The control method of sputum induction apparatus according to claim 4, wherein the calculation process of the neural network controller is:
step 1, predicting oscillation frequency η of parameters in sequencer+1And predicting the concussion intensity Ir+1Normalizing with the predicted pressure data;
step 2, determining an input layer neuron vector x ═ x of the three-layer BP neural network1,x2,x3In which x1To predict the oscillation frequency coefficient, x2To predict the oscillation intensity coefficient, x3Is a predicted pressure data index;
and 3, mapping the input layer vector to a hidden layer, wherein the hidden layer vector y is { y ═ y1,y2…ymM is the number of hidden nodes;
and 4, obtaining an output layer neuron vector o ═ o1,o2,o3}; wherein o is1Is the coefficient of sputum volume, o2Is the coefficient of consistency of sputum o3Is the sputum obstruction coefficient;
step 5, the controller outputs a control strategy relational expression of the next period;
wherein the output layer o3Has a neuron value of 0 or 1 when o3When the number is 1, the patient needs to be sputum-aspirated at this time, when the number is o3When 0, no treatment is performed.
9. The control method of the sputum induction apparatus according to claim 8, wherein the number m of nodes in the hidden layer satisfies:wherein n is the number of nodes of the input layer, and p is the number of nodes of the output layer.
10. The method of claim 8, wherein the oscillation frequency η is predictedr+1Predicting the oscillation intensity Ir+1And predicted pressure data Pλ+1The normalization formula of (a) is:
wherein x isjAs a parameter in the input layer vector, xjJ is 1,2,3 is the parameter η respectivelyr+1、Ir+1、Pλ+1；xjmaxAnd xjminRespectively, a maximum value and a minimum value in the corresponding measured parameter.
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| CN107456614A (en) * | 2017-08-08 | 2017-12-12 | 南通大学附属医院 | A kind of automatic alarm sputum aspirator |
| CN109497949A (en) * | 2018-12-12 | 2019-03-22 | 深圳融昕医疗科技有限公司 | Detection method, device, ventilator and the storage medium of apnea type |
| CN109646004A (en) * | 2019-01-29 | 2019-04-19 | 黄世英 | A kind of sputum intelligent-induction monitoring device |
| CN110327522A (en) * | 2019-07-22 | 2019-10-15 | 无锡圣诺亚科技有限公司 | A kind of tracheal catheter and automatic eliminating the phlegm device with automatic eliminating the phlegm function |
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| CN111543922A (en) * | 2020-06-12 | 2020-08-18 | 深圳市丰盛生物科技有限公司 | Portable laryngoscope with throat residual liquid detection function and residual liquid detection method |
| CN113509601A (en) * | 2021-07-15 | 2021-10-19 | 中国人民解放军西部战区总医院 | Negative pressure sputum suction equipment |
| CN113768530A (en) * | 2021-09-09 | 2021-12-10 | 无锡康莱医疗科技有限公司 | Phlegm-sound discrimination method |
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